Methodology

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1 Participants and Design

[Insert Name of Report] contains data from two studies. The first of these studies was a nationally representative survey of 10155 [Workers?] conducted by Opinium Research between 25th November 2023 and the 21st December 2023.

To achieve a robust estimate of outsourced workers, the sample was weighted by age, gender, and education, region, and ethnicity. The ethnic minority sub-sample (1,435 respondents) was also weighted separately by age, gender, and region to ensure that findings related to ethnic minority adults were fully representative. Targets were estimated using data from the Labour Force Survey, the 2021 Census for England and Wales, and the Northern Ireland Census.

This sample had median age 42 (SD = 13.02). 51% of respondents identified as female, 48% as male, 0.14% as other, and 0.65% preferred not to identify a gender. 76% of respondents identified as ‘English / Welsh / Scottish / Northern Irish / British’ (see Section 8.3 for a detailed breakdown of ethnicity).

A follow-up survey of Outsourced workers (as defined in Section 2.1) was conducted by Opinium Research between 19th April to the 16th of May 2024 with a total sample of 1814. The purpose of this study was to further probe the experiences of outsourced workers and to understand the impact of outsourcing on their work and lives (see Section 2.2).

Soft quotas on age, gender, and region were implemented to ensure broad representativeness, and the final data was weighted to targets based on age, gender, education, region, and ethnicity. The targets were based on the weighted data from study 1. The survey population had a mean Age of 38.9 (SD = 13.0). 42.4% Female and 65.5% White British. A small proportion of respondents had previously participated in study 1 and met the outsourced criteria (5%).

Both surveys were administered online.

An initial pilot study aimed to refine the diagnostic questions used to identify outsourced workers, ensuring they aligned with JRF’s initial definition and could be accurately answered by survey respondents. The diagnostic questions and feedback follow-ups were run on Opinium’s political omnibus, a nationally and politically representative sample of 2,055 UK adults between 30 August and 1 September 2023. The questions were filtered to those in work, resulting in a total of 1,200 respondents. Data from this pilot study is not reported here.

[POTENTIALLY ADD A TABLE HERE WITH CROSSTABS FOR THE TWO SAMPLES OR TABBED VISUALISATIONS]

2 Measures

2.1 Study 1- Nationally representative survey

The survey covered personal demographics, employment demographics (e.g. occupation, hours worked, pay), and the outsourced diagnostic questions. The main objectives were to ensure an accurate estimate of the size and demographic makeup of the outsourced population, and to analyse the data alongside the Labour Force Survey (LFS). [MORGAN - WHAT EXACTLY WAS INTENDED TO BE COMPARABLE? SPECIFICALLY WHICH QUESTIONS HAVE BEEN REPLICATED FROM TLFS]

Comparability to the LFS posed challenges, primarily because the LFS is conducted face-to-face, with interviewers playing a significant role in ensuring the accuracy of data and respondents’ understanding of questions. However, as the Transformed Labour Force Survey (TLFS)– an online first version of the survey set to replace the LFS— was underway, where possible we used the TLFS versions. While question wording is still under review, this was deemed the best approach, as some TLFS waves had already taken place and findings on comparability to LFS [MORGAN - CITATION].

• I believe some questions were replicated from national survey’s so lets explicate that
• Discuss the issue of pay calcs (e.g. basic overview and then refer to the section on it)
• Discuss the issue of defining outsourcing ((e.g. basic overview and then refer to the section on it))
• link to the data dictionary

2.1.1 Income calculations ([perhaps more detailed than necessary in this section])

Respondents could choose how they provided information about their income. Firstly, they could choose the payment period for which to express their income from the following options:

• Annually / per year
• Monthly
• Weekly
• Hourly

Secondly, they could choose either an ‘open’ form of reporting or a ‘closed’ form. The open form required respondents to type in their pay for the payment period they chose. The closed form required respondents to select which income bracket their pay belonged to from a list of options.

The annual options were:

• Less than £5,600 a year
• £5,600 up to £11,200
• £11,201 up to £16,800
• £16,801 up to £22,400
• £22,401 up to £28,000
• £28,001 up to £33,600
• £33,601 up to £39,200
• £39,201 up to £44,800
• £44,801 up to £50,400
• £50,401 up to £56,000
• Over £56,000 a year
• Prefer not to say

The monthly options were:

• Less than £470 a month
• £470 up to £940
• £941 up to £1,410
• £1,411 up to £1,880
• £1,881 up to £2,350
• £2,351 up to £2,820
• £2,821 up to £3,290
• £3,291 up to £3,760
• £3,761 up to £4,230
• £4,231 up to £4,700
• Over £4,700 a month
• Prefer not to say

The weekly options were:

• Less than £110 a week
• £110 up to £220
• £221 up to £330
• £331 up to £440
• £441 up to £550
• £551 up to £660
• £661 up to £770
• £771 up to £880
• £881 up to £990
• £991 up to £1,100
• Over £1,100 a week
• Prefer not to say

The hourly options were:

• Less than £8.91 an hour
• £8.91 up to £10.00
• £10.01 up to £12.00
• £12.01 up to £14.00
• £14.01 up to £16.00
• £16.01 up to £18.00
• £18.01 up to £20.00
• £20.01 up to £22.00
• £22.01 up to £24.00
• £24.01 up to £27.00
• £27.01 up to £30.00
• Over £30.00 an hour
• Prefer not to say

7499 respondents answered using the open method. 1445 respondents answered using the closed method. 1211 did not answer either.

We equivalised respondents’ income across the reporting options in two steps. Firstly, we converted closed income responses to continuous numeric values by taking the midpoint of the income brackets, or the value of the “less than” and “over” values. For example, a closed response of “£5,600 up to £11,200” would be converted to £8400; and a closed response of “Less than £5,600 a year” would be converted to £5600. These converted closed responses were combined with the open responses to produce a single continous income variable across payment periods.

Next, we expressed all respondents’ income in annual, weekly, and hourly periods. To do this we made an assumption about the number of working weeks in a year based on the minimum holiday entitlement of 28 days. We calculated the total number of weeks in a year as 365 / 7 = 52.14, the total number of non-working weeks as 28 / 5 = 5.6, and thus the total number of working weeks as 52.14 - 5.6 = 46.54.

With this figure and the number of hours worked per week, we could convert incomes provided in one payment period to another. The table below shows how this was achieved.

[ADD OUTLIER EXCLUSION CRITERIA]

Income provided...	Formula to convert to annual	Formula to convert to weekly	Formula to convert to hourly
... annually	= income	= income / working weeks	= weekly income / hours worked per week
... monthly	= income x 12	= (income x 12) / working weeks	= weekly income / hours worked per week
... weekly	= income x working weeks	= income	= weekly income / hours worked per week
... hourly	= income x hours per week x working weeks	= income x hours per week	= weekly income / hours worked per week

2.2 Study 2 - Outsourced workers survey

In the follow up survey of outsourced workers the data focuses on workers experiences and perceptions of outsourced work. The dataset is large containing 214 variables. Analysis of all the variables was beyond the scope of the report thus we focus on a subset of the data pertaining to outsourced workers experiences of rights violations, discrimination, job clarity, benefits and drawbacks of outsourced work and potential improvements to their work arrangements.

In the process of data cleaning we set hours per week to NA for participants who gave an impossible number of work hours per week (e.g. \(\ge\) 168, N=11). Relatedly we construct variables to determine hourly, weekly, monthly and annual pay as in study 1 and flag outlier responses. Through this method 11.22% (183) participants were dropped from all subsequent analysis leaving a final sample of 1631 participants. We also determine whether the participant is low paid using the method from study 1.

A data dictionary is available from the Github Repository associated with this project along with all code used to produce the analyses.

3 Analysis - Study 1

• Determining if the worker is outsourced
• Calculating Low/Not Low pay
• Explain the analyses (e.g. any models that actually appear in the final output)

3.1 Defining outsourcing

Workers were defined as outsourced based on responses to a set of diagnostic questions. Three questions asked respondents directly about whether they considered themselves outsourced and/or agency workers.

The first of these questions asked respondents to indicate directly whether they considered themselves outsourced by selecting one of the following options:

1. I am sure I’m an outsourced worker
2. I think I might be an outsourced worker
3. I am not an outsourced worker

The second question asked respondents to indicate whether they considered themselves an agency worker by selecting from three options. For respondents who responded 1 or 2 to question 1, the options were:

1. I am sure that I’m also an agency worker
2. I think I might also be an agency worker
3. I am not an agency worker

For respondents who responded 3 to question 1, the options were:

1. I am sure that I’m an agency worker
2. I think I might be an agency worker
3. I am not an agency worker

Respondents were also asked whether the work they do was long- or short-term by selecting one of:

1. I’m hired to do work which an organisation needs doing on a long-term or ongoing basis.
2. I’m hired to do work which an organisation needs doing on a short-term or temporary basis.
3. Other (please specify)

Finally, respondents were asked about aspects of their work that might indicate that the work they do is outsourced work. Respondents were asked: “Please read each of the following statements and tell us whether or not they are true for you and your work.” The statements were:

1. I am paid by one organisation but I do work for a different organisation.
2. The organisation I’m paid by is a ‘third party’ organisation which other organisations hire to do work for them, rather than doing that w [FIND QUESTION IN DATA DICT]
3. My employer / agency provides people to do work for other organisations (i.e. they might provide people to do cleaning, security, administratio [FIND QUESTION IN DATA DICT]
4. On a day-to-day basis, I’m paid by one organisation but I get given tasks or instructions by people who are paid by a different organisation.
5. I am paid by one organisation, but I work in a space which has the logo or branding of a different organisation.
6. I wear a uniform which has the logo or branding of my employer / agency, and which marks me out as being paid by a different organisation to so [FIND QUESTION IN DATA DICT]

Workers were categorised into three mutually exclusive sub groups based on their responses to the above questions.

1. A respondent was categorised as ‘clearly outsourced’ if they responded ‘I am sure I’m an outsourced worker’ or ‘I think I might be an outsourced worker’ and ‘I’m hired to do work which an organisation needs doing on a long-term or ongoing basis.’.

2. A respondent was categorised as ‘likely agency’ if they responded ‘I am sure that I’m an agency worker’ and ‘I’m hired to do work which an organisation needs doing on a long-term or ongoing basis’, excluding those people who are already defined as being ‘clearly outsourced’.

3. A respondent was categorised as belonging to the ‘high indicators’ group if they responded TRUE to five or six [CAN THIS BE EXPRESSED AS \(\ge\) 5?] of the outsourcing indicators, as well as responding ‘I’m hired to do work which an organisation needs doing on a long-term or ongoing basis’, excluding those people who were already defined as ‘clearly outsourced’ or ‘likely agency’.

Together, these three sub groups form the classification of ‘outsourced workers’ considered in this report. Throughout the report, the term ‘outsourced’ refers to workers across the three sub groups. In places, analysis considers the three sub groups separately, in which case the groups will be referred to by name as ‘clearly outsourced’, ‘likely agency’, or ‘high indicators’.

3.2 Defining low pay

A ‘low pay’ binary variable was created by implementing an income threshold below which respondents were considered to be on a relatively low income. In line with with the Organisation for Economic Co-operation and Development, we set the threshold at two-thirds median weekly income. The two-thirds threshold was based on the weekly median income for respondents’ region to account for regional variations in earnings.

Regional weekly median income values were drawn from the Annual Survey of Hours and Earnings (2023 provisional edition). Respondents whose reported weekly income was less than or equal to two-thirds of the median weekly income in their region were assigned to the ‘low pay’ group, while those whose reported weekly income was greater than two-thirds of the median weekly income in their region were assigned to the ‘not low pay’ group.

3.3 Aggregating ethnicity

For reference, the table below provides a disambiguation of how ethnicities have been grouped in this analysis.

For analyses using the disaggregated (survey) categories with 21 levels, the reference category is “English / Welsh / Scottish / Northern Irish / British”.

For analyses using the aggregated categories with 9 levels, the reference category is “White British”

For analyses using teh aggregated categories with 4 levels, the reference category is “White”.

Ethnicity: Survey (21 levels)	Ethnicity: Aggregated (9 levels)	Ethnicity: Binary (4 levels)
English / Welsh / Scottish / Northern Irish / British	White British	White
Irish	White other	White
Gypsy or Irish Traveller	White other	White
Roma	White other	White
Any other White background	White other	White
White and Black Caribbean	Mixed/Multiple ethnic group	Non-White
White and Black African	Mixed/Multiple ethnic group	Non-White
White and Asian	Mixed/Multiple ethnic group	Non-White
Any other Mixed / Multiple ethnic background	Mixed/Multiple ethnic group	Non-White
Indian	Asian/Asian British	Non-White
Pakistani	Asian/Asian British	Non-White
Bangladeshi	Asian/Asian British	Non-White
Chinese	Asian/Asian British	Non-White
Any other Asian background	Asian/Asian British	Non-White
African	Black/African/Caribbean/Black British	Non-White
Caribbean	Black/African/Caribbean/Black British	Non-White
Any other Black, Black British, or Caribbean background	Black/African/Caribbean/Black British	Non-White
Arab	Arab/British Arab	Non-White
Any other ethnic group	Other ethnic group	Non-White
Don’t think of myself as any of these	Don't think of myself as any of these	Don't think of myself as any of these
Prefer not to say	Prefer not to say	Prefer not to say
NA	NA	NA

3.4 Models

In this section we describe the statistical models used in the report. In all models we applied survey weights so that the estimates can be considered representative of employees nationally.

3.4.1 Outsourced pay gap

To investigate the pay gap been outsourced and non-outsourced workers we constructed a linear regression model predicting annual and weekly income (in separate models) from outsourcing membership. We included other variables in the model to account for their potential influence on income. The full regression model can be expressed as:

\[ Income = Age + Gender + Education + Ethnicity + Migration + Region + Outsourcing \]

where

• Income is a continuous numeric variable indicating a the respondent’s income (weekly or annual, in different models)
• Age is a continuous numeric variable indicating the respondent’s age
• Gender is a categorical variable with three levels:
  • Male (reference category)
  • Female
  • Other
• Education is a categorical variable indicating whether the respondent has a degree, with three levels:
  • Yes (reference category)
  • No
  • Don’t know
• Ethnicity is a categorical variable with eight levels:
  • White British (reference category)
  • Arab/British Arab
  • Asian/Asian British
  • Black/African/Caribbean/Black British
  • Mixed/Multiple ethnic group
  • Other ethnic group
  • Prefer not to say
  • White other
  • Don’t think of myself as any of these
• Migration is a categorical variable indicating when the respondent arrived in the UK, with 10 levels:
  • I was born in the UK (reference category)
  • Within the last year
  • Within the last 3 years
  • Within the last 5 years
  • Within the last 10 years
  • Within the last 15 years
  • Within the last 20 years
  • Within the last 30 years
  • More than 30 years ago
  • Prefer not to say
• Region is a categorical variable indicating the respodent’s region of residence, with 12 levels:
  • London (reference category)
  • East Midlands
  • East of England
  • North East
  • North West
  • Northern Ireland
  • Scotland
  • South East
  • South West
  • Wales
  • West Midlands
  • Yorkshire and the Humber
• Outsourcing is a categorical variable indicating whether the respondent is outsourced, with two levels:
  • Not outsourced (reference category)
  • Outsourced

The annual income model was statistically significant (R² = 0.18, F(35, 8071) = 51.29, p < .001). The table below shows the coefficients for the annual income model.

	Annual income
Predictors	Estimates	CI	p
Intercept	39068.13	37794.38 – 40341.88	<0.001
Age	14.39	-6.41 – 35.19	0.175
Gender: Female	-7002.82	-7535.16 – -6470.48	<0.001
Gender: Other	-6032.87	-12748.83 – 683.09	0.078
Gender: Prefer not to say	-2828.76	-9792.72 – 4135.20	0.426
Education: Don't have degree	-8170.64	-8723.33 – -7617.95	<0.001
Education: Don't know	-9849.13	-12104.71 – -7593.55	<0.001
Ethnicity: Arab/British Arab	-177.61	-4873.46 – 4518.23	0.941
Ethnicity: Asian/Asian British	-471.78	-1573.79 – 630.22	0.401
Ethnicity: Black/African/Caribbean/Black British	-1203.90	-2816.77 – 408.97	0.143
Ethnicity: Don't think of myself as any of these	-3198.11	-12756.84 – 6360.62	0.512
Ethnicity: Mixed/Multiple ethnic group	-1507.68	-3488.56 – 473.20	0.136
Ethnicity: Other ethnic group	3596.90	-998.30 – 8192.10	0.125
Ethnicity: Prefer not to say	-82.72	-5289.34 – 5123.89	0.975
Ethnicity: White other	-637.07	-2018.88 – 744.74	0.366
Region: East Midlands	-5854.69	-7085.16 – -4624.23	<0.001
Region: East of England	-4103.34	-5262.01 – -2944.67	<0.001
Region: North East	-4834.89	-6372.61 – -3297.16	<0.001
Region: North West	-4472.28	-5597.32 – -3347.24	<0.001
Region: Northern Ireland	-6336.40	-8132.24 – -4540.55	<0.001
Region: Scotland	-5448.95	-6649.58 – -4248.32	<0.001
Region: South East	-3460.88	-4512.27 – -2409.49	<0.001
Region: South West	-5748.69	-6947.04 – -4550.34	<0.001
Region: Wales	-5215.03	-6681.40 – -3748.66	<0.001
Region: West Midlands	-4759.33	-5932.19 – -3586.48	<0.001
Region: Yorkshire and the Humber	-5451.06	-6649.03 – -4253.09	<0.001
Outsourcing: Outsourced	-2995.19	-3715.49 – -2274.89	<0.001
Migration: Arrived within the last year	-6032.95	-8309.80 – -3756.10	<0.001
Migration: Arrived within the last 3 years	-2375.85	-4406.64 – -345.06	0.022
Migration: Arrived within the last 5 years	-1830.71	-4132.93 – 471.51	0.119
Migration: Arrived within the last 10 years	-691.71	-2485.65 – 1102.24	0.450
Migration: Arrived within the last 15 years	747.68	-1267.29 – 2762.65	0.467
Migration: Arrived within the last 20 years	1625.36	-508.65 – 3759.37	0.135
Migration: Arrived within the last 30 years	2911.95	401.12 – 5422.79	0.023
Migration: Arrived more than 30 years ago	-46.10	-2002.94 – 1910.74	0.963
Migration: Prefer not to say	-1667.72	-5284.42 – 1948.98	0.366
Observations	8107
R2 / R2 adjusted	0.182 / 0.178

As expected, the model statistics for weekly income model were identical to the those of the annual income model. The model was statistically significant (R² = 0.18, F(35, 8071) = 51.29, p < .001). The table below shows the coefficients for the weekly income model.

	Weekly income
Predictors	Estimates	CI	p
Intercept	839.40	812.03 – 866.77	<0.001
Age	0.31	-0.14 – 0.76	0.175
Gender: Female	-150.46	-161.90 – -139.02	<0.001
Gender: Other	-129.62	-273.92 – 14.68	0.078
Gender: Prefer not to say	-60.78	-210.40 – 88.85	0.426
Education: Don't have degree	-175.55	-187.43 – -163.68	<0.001
Education: Don't know	-211.61	-260.08 – -163.15	<0.001
Ethnicity: Arab/British Arab	-3.82	-104.71 – 97.08	0.941
Ethnicity: Asian/Asian British	-10.14	-33.81 – 13.54	0.401
Ethnicity: Black/African/Caribbean/Black British	-25.87	-60.52 – 8.79	0.143
Ethnicity: Don't think of myself as any of these	-68.71	-274.09 – 136.66	0.512
Ethnicity: Mixed/Multiple ethnic group	-32.39	-74.95 – 10.17	0.136
Ethnicity: Other ethnic group	77.28	-21.45 – 176.01	0.125
Ethnicity: Prefer not to say	-1.78	-113.64 – 110.09	0.975
Ethnicity: White other	-13.69	-43.38 – 16.00	0.366
Region: East Midlands	-125.79	-152.23 – -99.35	<0.001
Region: East of England	-88.16	-113.06 – -63.27	<0.001
Region: North East	-103.88	-136.92 – -70.84	<0.001
Region: North West	-96.09	-120.26 – -71.92	<0.001
Region: Northern Ireland	-136.14	-174.73 – -97.56	<0.001
Region: Scotland	-117.07	-142.87 – -91.28	<0.001
Region: South East	-74.36	-96.95 – -51.77	<0.001
Region: South West	-123.51	-149.26 – -97.77	<0.001
Region: Wales	-112.05	-143.55 – -80.54	<0.001
Region: West Midlands	-102.26	-127.46 – -77.06	<0.001
Region: Yorkshire and the Humber	-117.12	-142.86 – -91.38	<0.001
Outsourcing: Outsourced	-64.35	-79.83 – -48.88	<0.001
Migration: Arrived within the last year	-129.62	-178.54 – -80.70	<0.001
Migration: Arrived within the last 3 years	-51.05	-94.68 – -7.41	0.022
Migration: Arrived within the last 5 years	-39.33	-88.80 – 10.13	0.119
Migration: Arrived within the last 10 years	-14.86	-53.41 – 23.68	0.450
Migration: Arrived within the last 15 years	16.06	-27.23 – 59.36	0.467
Migration: Arrived within the last 20 years	34.92	-10.93 – 80.77	0.135
Migration: Arrived within the last 30 years	62.56	8.62 – 116.51	0.023
Migration: Arrived more than 30 years ago	-0.99	-43.03 – 41.05	0.963
Migration: Prefer not to say	-35.83	-113.54 – 41.87	0.366
Observations	8107
R2 / R2 adjusted	0.182 / 0.178

3.4.2 Gender pay gap

The above model was also used to assess a possible gender pay gap. As shown in the preceding two tables, there is a significant difference in pay between men and women. Annually, women earn £7002.82 less than men. Per week, women earn £150.46 less than men.

We next explored whether outsourcing compounds this gender pay gap by adding an interaction term into the previous models so that

\[ Income = Age + Gender + Education + Ethnicity + Migration + Region + Outsourcing + Gender:Outsourcing \]

For both models, adding the interaction effect did not improve model fit (R² = 0.18, F(3, 8068) = 0.74, p = 0.531). The tables below show the coefficients for each model.

	Annual income
Predictors	Estimates	CI	p
Intercept	39092.47	37809.50 – 40375.44	<0.001
Age	14.30	-6.51 – 35.10	0.178
Gender: Female	-7004.18	-7586.05 – -6422.30	<0.001
Gender: Other	-3445.67	-10995.26 – 4103.92	0.371
Gender: Prefer not to say	-2634.20	-9886.89 – 4618.49	0.477
Education: Has degree	-8169.16	-8722.09 – -7616.22	<0.001
Education: Don't know	-9849.18	-12104.91 – -7593.45	<0.001
Ethnicity: Arab/British Arab	-170.96	-4867.74 – 4525.81	0.943
Ethnicity: Asian/Asian British	-472.69	-1574.78 – 629.39	0.401
Ethnicity: Black/African/Caribbean/Black British	-1203.91	-2816.96 – 409.13	0.143
Ethnicity: Don't think of myself as any of these	-3193.67	-12753.23 – 6365.88	0.513
Ethnicity: Mixed/Multiple ethnic group	-1511.45	-3492.47 – 469.58	0.135
Ethnicity: Other ethnic group	3593.79	-1002.26 – 8189.84	0.125
Ethnicity: Prefer not to say	-81.55	-5288.77 – 5125.67	0.976
Ethnicity: White other	-601.41	-1984.70 – 781.89	0.394
Region: East Midlands	-5879.63	-7110.82 – -4648.44	<0.001
Region: East of England	-4135.62	-5295.46 – -2975.79	<0.001
Region: North East	-4865.76	-6404.23 – -3327.29	<0.001
Region: North West	-4502.26	-5628.38 – -3376.14	<0.001
Region: Northern Ireland	-6358.52	-8158.35 – -4558.69	<0.001
Region: Scotland	-5476.68	-6678.10 – -4275.26	<0.001
Region: South East	-3488.28	-4540.37 – -2436.18	<0.001
Region: South West	-5772.53	-6971.45 – -4573.60	<0.001
Region: Wales	-5238.94	-6705.78 – -3772.10	<0.001
Region: West Midlands	-4783.07	-5956.82 – -3609.31	<0.001
Region: Yorkshire and the Humber	-5477.80	-6676.53 – -4279.07	<0.001
Outsourcing: Outsourced	-2979.46	-3945.17 – -2013.76	<0.001
Migration: Arrived within the last year	-6043.23	-8320.40 – -3766.06	<0.001
Migration: Arrived within the last 3 years	-2386.09	-4418.13 – -354.05	0.021
Migration: Arrived within the last 5 years	-1849.05	-4152.16 – 454.06	0.116
Migration: Arrived within the last 10 years	-719.66	-2514.13 – 1074.81	0.432
Migration: Arrived within the last 15 years	718.31	-1297.19 – 2733.81	0.485
Migration: Arrived within the last 20 years	1602.21	-532.38 – 3736.80	0.141
Migration: Arrived within the last 30 years	2893.95	382.76 – 5405.14	0.024
Migration: Arrived more than 30 years ago	-58.23	-2016.55 – 1900.09	0.954
Migration: Prefer not to say	-1683.14	-5300.38 – 1934.09	0.362
Interaction: Outsourcing x Gender Female	18.18	-1412.01 – 1448.37	0.980
Interaction: Outsourcing x Gender Other	-12395.16	-28915.23 – 4124.91	0.141
Interaction: Outsourcing x Gender Prefer not to say	-2506.28	-28505.03 – 23492.47	0.850
Observations	8107
R2 / R2 adjusted	0.182 / 0.178

	Weekly income
Predictors	Estimates	CI	p
Intercept	839.92	812.36 – 867.49	<0.001
Age	0.31	-0.14 – 0.75	0.178
Gender: Female	-150.49	-162.99 – -137.99	<0.001
Gender: Other	-74.03	-236.24 – 88.18	0.371
Gender: Prefer not to say	-56.60	-212.43 – 99.23	0.477
Education: Has degree	-175.52	-187.40 – -163.64	<0.001
Education: Don't know	-211.62	-260.08 – -163.15	<0.001
Ethnicity: Arab/British Arab	-3.67	-104.59 – 97.24	0.943
Ethnicity: Asian/Asian British	-10.16	-33.84 – 13.52	0.401
Ethnicity: Black/African/Caribbean/Black British	-25.87	-60.52 – 8.79	0.143
Ethnicity: Don't think of myself as any of these	-68.62	-274.01 – 136.77	0.513
Ethnicity: Mixed/Multiple ethnic group	-32.47	-75.04 – 10.09	0.135
Ethnicity: Other ethnic group	77.21	-21.53 – 175.96	0.125
Ethnicity: Prefer not to say	-1.75	-113.63 – 110.13	0.976
Ethnicity: White other	-12.92	-42.64 – 16.80	0.394
Region: East Midlands	-126.33	-152.78 – -99.87	<0.001
Region: East of England	-88.86	-113.78 – -63.94	<0.001
Region: North East	-104.54	-137.60 – -71.49	<0.001
Region: North West	-96.73	-120.93 – -72.54	<0.001
Region: Northern Ireland	-136.62	-175.29 – -97.95	<0.001
Region: Scotland	-117.67	-143.48 – -91.86	<0.001
Region: South East	-74.95	-97.55 – -52.34	<0.001
Region: South West	-124.03	-149.79 – -98.27	<0.001
Region: Wales	-112.56	-144.08 – -81.05	<0.001
Region: West Midlands	-102.77	-127.99 – -77.55	<0.001
Region: Yorkshire and the Humber	-117.69	-143.45 – -91.94	<0.001
Outsourcing: Outsourced	-64.02	-84.76 – -43.27	<0.001
Migration: Arrived within the last year	-129.84	-178.77 – -80.92	<0.001
Migration: Arrived within the last 3 years	-51.27	-94.93 – -7.61	0.021
Migration: Arrived within the last 5 years	-39.73	-89.21 – 9.76	0.116
Migration: Arrived within the last 10 years	-15.46	-54.02 – 23.09	0.432
Migration: Arrived within the last 15 years	15.43	-27.87 – 58.74	0.485
Migration: Arrived within the last 20 years	34.42	-11.44 – 80.29	0.141
Migration: Arrived within the last 30 years	62.18	8.22 – 116.13	0.024
Migration: Arrived more than 30 years ago	-1.25	-43.33 – 40.82	0.954
Migration: Prefer not to say	-36.16	-113.88 – 41.56	0.362
Interaction: Outsourcing x Gender Female	0.39	-30.34 – 31.12	0.980
Interaction: Outsourcing x Gender Other	-266.32	-621.26 – 88.63	0.141
Interaction: Outsourcing x Gender Prefer not to say	-53.85	-612.45 – 504.75	0.850
Observations	8107
R2 / R2 adjusted	0.182 / 0.178

The interaction term is non-significant. Estimated marginal means show that:

• Among not outsourced workers, men are paid £7004.18 more than women
• Among outsourced workers, men are paid £6986 more than women
• Among men, not outsourced workers are paid £2979.46 more than outsourced workers.
• Among women, not outsourced workers are paid £2961.28 more than outsourced workers.

The plot below illustrates the main effects that men are paid more than women and that outsourced men and women are paid less than non-outsourced men and women. The lack of interaction indicates that the difference in pay between men and women does not significantly differ between outsourced and non-outsourced people.

3.4.3 Demographic models

3.4.3.1 Ethnicity

Several regressions were run to assess the likelihood of being outsourced from demographics. These models underlie the claims in the report in relation to ethnicity, migration, and gender.

The overall model was defined as:

\[ Outsourcing = Ethnicity + Age + Gender + Education + Region + Migration \]

where

• Outsourcing is a categorical variable indicating whether the respondent is outsourced, with two levels:
  • Not outsourced (reference category)
  • Outsourced
• Age is a continuous numeric variable indicating the respondent’s age
• Gender is a categorical variable with three levels:
  • Male (reference category)
  • Female
  • Other
• Education is a categorical variable indicating whether the respondent has a degree, with three levels:
  • Yes (reference category)
  • No
  • Don’t know
• Migration is a categorical variable indicating when the respondent arrived in the UK, with 10 levels:
  • I was born in the UK (reference category)
  • Within the last year
  • Within the last 3 years
  • Within the last 5 years
  • Within the last 10 years
  • Within the last 15 years
  • Within the last 20 years
  • Within the last 30 years
  • More than 30 years ago
  • Prefer not to say
• Region is a categorical variable indicating the respodent’s region of residence, with 12 levels:
  • London (reference category)
  • East Midlands
  • East of England
  • North East
  • North West
  • Northern Ireland
  • Scotland
  • South East
  • South West
  • Wales
  • West Midlands
  • Yorkshire and the Humber

For this exploration we modelled ethnicity in three ways.

1. As a categorical variable with four levels:
   • White (reference category)
   • Not White
   • Don’t think of myself as any of these
   • Prefer not say
2. As a categorical variable with eight levels:
   • White British (reference category)
   • Arab/British Arab
   • Asian/Asian British
   • Black/African/Caribbean/Black British
   • Don’t think of myself as any of these
   • Mixed/Multiple ethnic group
   • Other ethnic group
   • Prefer not to say
   • White other
3. As a categorical variable with 21 levels:
   • English/Welsh/Scottish/Northern Irish/British (reference category)
   • Irish
   • Gypsy or Irish Traveller
   • Roma
   • Any other White background
   • White and Black Caribbean
   • White and Black African
   • White and Asian
   • Any other Mixed/Multiple ethnic background
   • Indian
   • Pakistani
   • Bangladeshi
   • Chinese
   • Any other Asian background
   • African
   • Caribbean
   • Any other Black, Black British, or Caribbean background
   • Arab
   • Any other ethnic group
   • Don’t think of myself as any of these
   • Prefer not to say

We used svyglm() from the survey package to construct survey-weighted generalised linear models. This approach allows us to take into account survey weights to produce design-based standard errors by assuming a ‘quasibinomial’ distribution to the data. Specifically, the survey-weighted data contains overdispersion; the variance is greater than expected by a binomial distribution (which assumes variance = mean(1 - mean)). The quasibinomial distribution estimates a dispersion parameter that allows the variance to be greater than expected by the true binomial distribution. For more information see Lumley, Thomas, and Alastair Scott. ‘Fitting Regression Models to Survey Data’. Statistical Science 32, no. 2 (2017): 265–780.

We used Rao–Scott adjusted Wald tests to compare nested survey-weighted models fit using a quasibinomial family. This method accounts for the survey design and is appropriate given that quasi-likelihood models do not support likelihood-ratio testing. For more information see Rao, J. N. K., and A. J. Scott. ‘On Chi-Squared Tests for Multiway Contingency Tables with Cell Proportions Estimated from Survey Data’. The Annals of Statistics 12, no. 1 (March 1984): 46–60. https://doi.org/10.1214/aos/1176346391.

For model 1, a saturated model including all variables was a significantly better fit to the data than an intercept-only model, F(29, 9782) = 8.72, p < .001. The table below shows the model coefficients.

	Outsourcing
Predictors	Odds Ratios	CI	p
Intercept	0.65	0.49 – 0.86	0.002
Ethnicity: Not White	1.38	1.15 – 1.66	0.001
Ethnicity: Don't think of myself as any of these	2.50	0.76 – 8.18	0.131
Ethnicity: Prefer not to say	1.41	0.39 – 5.14	0.600
Age	0.98	0.97 – 0.98	<0.001
Gender: Female	0.69	0.61 – 0.79	<0.001
Gender: Other	0.87	0.22 – 3.50	0.842
Gender: Prefer not to say	0.82	0.26 – 2.53	0.724
Education: Don't have degree	1.06	0.93 – 1.21	0.372
Education: Don't know	1.14	0.64 – 2.04	0.650
Region: East Midlands	0.94	0.71 – 1.25	0.676
Region: East of England	0.59	0.44 – 0.79	0.001
Region: North East	0.63	0.44 – 0.91	0.012
Region: North West	0.85	0.66 – 1.08	0.185
Region: Northern Ireland	0.70	0.46 – 1.07	0.098
Region: Scotland	0.68	0.50 – 0.93	0.015
Region: South East	0.62	0.49 – 0.79	<0.001
Region: South West	0.68	0.52 – 0.90	0.006
Region: Wales	0.91	0.66 – 1.25	0.551
Region: West Midlands	0.83	0.65 – 1.08	0.166
Region: Yorkshire and the Humber	0.68	0.52 – 0.89	0.005
Migration: Arrived within the last year	1.69	1.13 – 2.52	0.010
Migration: Arrived within the last 3 years	1.03	0.68 – 1.56	0.885
Migration: Arrived within the last 5 years	1.22	0.78 – 1.89	0.386
Migration: Arrived within the last 10 years	1.61	1.14 – 2.26	0.006
Migration: Arrived within the last 15 years	1.58	1.05 – 2.36	0.026
Migration: Arrived within the last 20 years	1.54	1.00 – 2.35	0.048
Migration: Arrived within the last 30 years	0.45	0.22 – 0.92	0.029
Migration: Arrived more than 30 years ago	2.01	1.31 – 3.08	0.001
Migration: Prefer not to say	1.22	0.71 – 2.10	0.472
Observations	9812

For model 2, a saturated model including all variables was a significantly better fit to the data than an intercept-only model, F(34, 9777) = 8.07, p < .001. The table below shows the model coefficients.

	Outsourcing
Predictors	Odds Ratios	CI	p
Intercept	0.67	0.50 – 0.88	0.004
Ethnicity: Arab/British Arab	1.98	0.77 – 5.11	0.159
Ethnicity: Asian/Asian British	1.24	0.96 – 1.59	0.094
Ethnicity: Black/African/Caribbean/Black British	1.47	1.10 – 1.96	0.010
Ethnicity: Don't think of myself as any of these	2.35	0.72 – 7.73	0.159
Ethnicity: Mixed/Multiple ethnic group	1.37	1.00 – 1.89	0.053
Ethnicity: Other ethnic group	1.03	0.27 – 4.01	0.963
Ethnicity: Prefer not to say	1.37	0.37 – 5.01	0.639
Ethnicity: White other	0.81	0.58 – 1.11	0.191
Age	0.98	0.97 – 0.98	<0.001
Gender: Female	0.69	0.61 – 0.78	<0.001
Gender: Other	0.88	0.21 – 3.62	0.860
Gender: Prefer not to say	0.81	0.26 – 2.50	0.711
Education: Don't have degree	1.06	0.93 – 1.21	0.357
Education: Don't know	1.16	0.65 – 2.06	0.618
Region: East Midlands	0.93	0.71 – 1.23	0.628
Region: East of England	0.58	0.43 – 0.78	<0.001
Region: North East	0.62	0.43 – 0.89	0.009
Region: North West	0.83	0.65 – 1.07	0.143
Region: Northern Ireland	0.72	0.47 – 1.10	0.129
Region: Scotland	0.67	0.50 – 0.91	0.011
Region: South East	0.61	0.48 – 0.78	<0.001
Region: South West	0.67	0.50 – 0.88	0.004
Region: Wales	0.89	0.65 – 1.22	0.476
Region: West Midlands	0.83	0.64 – 1.07	0.147
Region: Yorkshire and the Humber	0.67	0.51 – 0.88	0.004
Migration: Arrived within the last year	1.71	1.13 – 2.60	0.011
Migration: Arrived within the last 3 years	1.08	0.71 – 1.65	0.713
Migration: Arrived within the last 5 years	1.31	0.83 – 2.07	0.249
Migration: Arrived within the last 10 years	1.80	1.23 – 2.63	0.002
Migration: Arrived within the last 15 years	1.74	1.13 – 2.68	0.013
Migration: Arrived within the last 20 years	1.68	1.07 – 2.63	0.024
Migration: Arrived within the last 30 years	0.49	0.23 – 1.02	0.056
Migration: Arrived more than 30 years ago	2.09	1.36 – 3.22	0.001
Migration: Prefer not to say	1.24	0.72 – 2.14	0.430
Observations	9812

For model 3, a saturated model including all variables was a significantly better fit to the data than an intercept-only model, F(46, 9765) = 6.88, p < .001. The table below shows the model coefficients.

	Outsourcing
Predictors	Odds Ratios	CI	p
Intercept	0.67	0.50 – 0.88	0.004
Ethnicity: Irish	0.76	0.40 – 1.45	0.410
Ethnicity: Gypsy or Irish Traveller	1.00	0.18 – 5.50	0.999
Ethnicity: Roma	1.25	0.32 – 4.87	0.751
Ethnicity: Any other White background	0.80	0.55 – 1.15	0.225
Ethnicity: White and Black Caribbean	0.49	0.25 – 0.95	0.035
Ethnicity: White and Black African	2.66	1.51 – 4.66	0.001
Ethnicity: White and Asian	1.19	0.58 – 2.46	0.634
Ethnicity: Any other Mixed/Multiple ethnic background	2.00	1.08 – 3.67	0.026
Ethnicity: Indian	1.18	0.81 – 1.71	0.396
Ethnicity: Pakistani	2.17	1.45 – 3.25	<0.001
Ethnicity: Bangladeshi	1.32	0.71 – 2.45	0.375
Ethnicity: Chinese	0.62	0.33 – 1.19	0.150
Ethnicity: Any other Asian background	1.19	0.70 – 2.04	0.521
Ethnicity: African	1.44	1.04 – 2.00	0.027
Ethnicity: Caribbean	1.27	0.63 – 2.54	0.499
Ethnicity: Any other Black, Black British, or Caribbean background	1.84	0.88 – 3.86	0.106
Ethnicity: Arab	1.97	0.76 – 5.10	0.164
Ethnicity: Any other ethnic group	1.03	0.26 – 4.03	0.963
Ethnicity: Don't think of myself as any of these	2.31	0.71 – 7.57	0.166
Ethnicity: Prefer not to say	1.37	0.37 – 5.04	0.632
Age	0.98	0.97 – 0.98	<0.001
Gender: Female	0.69	0.61 – 0.79	<0.001
Gender: Other	0.88	0.22 – 3.63	0.865
Gender: Prefer not to say	0.83	0.27 – 2.56	0.747
Education: Don't have degree	1.06	0.93 – 1.20	0.402
Education: Don't know	1.16	0.65 – 2.06	0.623
Region: East Midlands	0.92	0.70 – 1.22	0.583
Region: East of England	0.57	0.42 – 0.77	<0.001
Region: North East	0.61	0.43 – 0.88	0.008
Region: North West	0.81	0.63 – 1.05	0.111
Region: Northern Ireland	0.73	0.46 – 1.14	0.165
Region: Scotland	0.67	0.49 – 0.91	0.009
Region: South East	0.60	0.48 – 0.77	<0.001
Region: South West	0.66	0.50 – 0.87	0.003
Region: Wales	0.88	0.64 – 1.21	0.423
Region: West Midlands	0.81	0.62 – 1.05	0.104
Region: Yorkshire and the Humber	0.65	0.50 – 0.86	0.002
Migration: Arrived within the last year	1.76	1.14 – 2.72	0.011
Migration: Arrived within the last 3 years	1.10	0.72 – 1.69	0.652
Migration: Arrived within the last 5 years	1.23	0.77 – 1.97	0.389
Migration: Arrived within the last 10 years	1.76	1.20 – 2.57	0.004
Migration: Arrived within the last 15 years	1.84	1.18 – 2.87	0.007
Migration: Arrived within the last 20 years	1.69	1.07 – 2.66	0.024
Migration: Arrived within the last 30 years	0.49	0.24 – 1.01	0.054
Migration: Arrived more than 30 years ago	2.11	1.37 – 3.26	0.001
Migration: Prefer not to say	1.22	0.70 – 2.12	0.478
Observations	9812

3.4.3.2 Migration

We next focus on predicting whether a person was outsourced based on wehther the person was born in the UK. This binary variable was constructed by collapsing the 10-level migration variable down into two levels, so that “I was born in the UK” becomes “Born in UK”, and all levels apart from “I was born in the UK” and “Prefer not to say” become “Not born in UK”.

A saturated model including all variables was a significantly better fit to the data than an intercept-only model, F(39, 9772) = 7.52, p < .001. The table below shows the model coefficients.

	Outsourcing
Predictors	Odds Ratios	CI	p
Intercept	0.66	0.50 – 0.88	0.004
Migration: Not born in the UK	1.52	1.22 – 1.89	<0.001
Migration: Prefer not to say	1.23	0.71 – 2.13	0.465
Ethnicity: Irish	0.76	0.40 – 1.44	0.396
Ethnicity: Gypsy or Irish Traveller	1.04	0.20 – 5.55	0.962
Ethnicity: Roma	1.11	0.26 – 4.73	0.883
Ethnicity: Any other White background	0.82	0.57 – 1.16	0.253
Ethnicity: White and Black Caribbean	0.48	0.25 – 0.94	0.033
Ethnicity: White and Black African	2.56	1.45 – 4.54	0.001
Ethnicity: White and Asian	1.22	0.58 – 2.57	0.595
Ethnicity: Any other Mixed/Multiple ethnic background	1.81	1.04 – 3.16	0.037
Ethnicity: Indian	1.13	0.77 – 1.65	0.530
Ethnicity: Pakistani	2.13	1.40 – 3.22	<0.001
Ethnicity: Bangladeshi	1.27	0.68 – 2.37	0.457
Ethnicity: Chinese	0.60	0.31 – 1.15	0.125
Ethnicity: Any other Asian background	1.21	0.72 – 2.04	0.480
Ethnicity: African	1.46	1.08 – 1.98	0.014
Ethnicity: Caribbean	1.24	0.61 – 2.51	0.545
Ethnicity: Any other Black, Black British, or Caribbean background	1.73	0.83 – 3.64	0.146
Ethnicity: Arab	2.04	0.80 – 5.22	0.136
Ethnicity: Any other ethnic group	1.04	0.27 – 4.01	0.951
Ethnicity: Don't think of myself as any of these	2.28	0.71 – 7.27	0.164
Ethnicity: Prefer not to say	1.29	0.35 – 4.77	0.704
Age	0.98	0.97 – 0.98	<0.001
Gender: Female	0.70	0.61 – 0.79	<0.001
Gender: Other	0.88	0.22 – 3.62	0.865
Gender: Prefer not to say	0.82	0.27 – 2.54	0.737
Education: Don't have degree	1.05	0.93 – 1.20	0.420
Education: Don't know	1.19	0.67 – 2.11	0.561
Region: East Midlands	0.93	0.70 – 1.23	0.598
Region: East of England	0.56	0.42 – 0.76	<0.001
Region: North East	0.61	0.42 – 0.88	0.007
Region: North West	0.81	0.63 – 1.04	0.100
Region: Northern Ireland	0.72	0.46 – 1.13	0.157
Region: Scotland	0.66	0.49 – 0.90	0.008
Region: South East	0.60	0.47 – 0.76	<0.001
Region: South West	0.66	0.50 – 0.87	0.003
Region: Wales	0.87	0.64 – 1.20	0.403
Region: West Midlands	0.80	0.62 – 1.04	0.098
Region: Yorkshire and the Humber	0.65	0.49 – 0.85	0.002
Observations	9812

3.4.3.3 Gender

We used the same generalised linear model as in the previous section to estimate the effect of Gender on outsourcing, where Gender is a categorical variable with four levels:

• Male (reference category)
• Female
• Prefer not to say
• Other

The model indicates that women are 0.7 times as likely (i.e. 30% less likely) to be outsourced than men.

3.4.3.4 Age

Again using the same model, we found that age was a significant predictor of the likelihood of being outsourced. The model indicates that for each year older a worker is, they are 0.98 times as likely (i.e. 2% less likely) to be outsourced.

We also explored how age predicted whether a person was on low pay. The model formula is:

\[ Income Group = Age + Outsourcing + Ethnicity + Gender + Education + Region + Migration \]

A saturated model including all variables was a significantly better fit to the data than an intercept-only model, X^2(80) = 22488.5220035, p < .001. The table below shows the model coefficients.

income_group	estimate	std.error	statistic	p.value	conf.low	conf.high	sig
Mid
(Intercept)	0.134	0.171	-11.740	0.000	0.096	0.187	***
Age	1.007	0.003	2.637	0.008	1.002	1.012	**
outsourcing_statusOutsourced	1.362	0.088	3.504	0.000	1.146	1.619	***
Ethnicity_collapsed_disaggregatedIrish	1.347	0.297	1.003	0.316	0.753	2.412
Ethnicity_collapsed_disaggregatedGypsy or Irish Traveller	0.253	1.063	-1.295	0.195	0.031	2.028
Ethnicity_collapsed_disaggregatedRoma	0.000	0.211	-70.286	0.000	0.000	0.000	***
Ethnicity_collapsed_disaggregatedAny other White background	1.032	0.195	0.163	0.870	0.704	1.514
Ethnicity_collapsed_disaggregatedWhite and Black Caribbean	1.227	0.337	0.609	0.543	0.635	2.374
Ethnicity_collapsed_disaggregatedWhite and Black African	0.971	0.460	-0.064	0.949	0.394	2.392
Ethnicity_collapsed_disaggregatedWhite and Asian	1.770	0.324	1.765	0.077	0.939	3.337
Ethnicity_collapsed_disaggregatedAny other Mixed / Multiple ethnic background	1.906	0.332	1.940	0.052	0.993	3.657
Ethnicity_collapsed_disaggregatedIndian	0.841	0.228	-0.761	0.446	0.538	1.314
Ethnicity_collapsed_disaggregatedPakistani	1.313	0.249	1.092	0.275	0.805	2.141
Ethnicity_collapsed_disaggregatedBangladeshi	1.612	0.378	1.262	0.207	0.768	3.385
Ethnicity_collapsed_disaggregatedChinese	0.718	0.362	-0.916	0.360	0.353	1.459
Ethnicity_collapsed_disaggregatedAny other Asian background	1.266	0.360	0.656	0.512	0.625	2.564
Ethnicity_collapsed_disaggregatedAfrican	1.246	0.198	1.110	0.267	0.845	1.838
Ethnicity_collapsed_disaggregatedCaribbean	0.938	0.396	-0.163	0.870	0.432	2.036
Ethnicity_collapsed_disaggregatedAny other Black, Black British, or Caribbean background	1.195	0.475	0.376	0.707	0.471	3.030
Ethnicity_collapsed_disaggregatedArab	1.715	0.661	0.816	0.415	0.469	6.263
Ethnicity_collapsed_disaggregatedAny other ethnic group	0.629	1.158	-0.401	0.689	0.065	6.081
Ethnicity_collapsed_disaggregatedDon’t think of myself as any of these	0.350	0.897	-1.171	0.241	0.060	2.028
Ethnicity_collapsed_disaggregatedPrefer not to say	1.676	0.647	0.799	0.424	0.472	5.951
GenderFemale	2.754	0.071	14.266	0.000	2.396	3.165	***
GenderOther	3.946	0.663	2.071	0.038	1.076	14.468	*
GenderPrefer not to say	2.024	0.841	0.838	0.402	0.389	10.525
Has_DegreeNo	1.912	0.067	9.684	0.000	1.677	2.180	***
Has_DegreeDon't know	3.073	0.304	3.693	0.000	1.693	5.576	***
RegionEast Midlands	1.106	0.153	0.662	0.508	0.820	1.492
RegionEast of England	1.124	0.158	0.740	0.459	0.825	1.531
RegionNorth East	0.838	0.192	-0.920	0.358	0.575	1.221
RegionNorth West	0.760	0.146	-1.881	0.060	0.571	1.012
RegionNorthern Ireland	1.122	0.213	0.540	0.589	0.739	1.702
RegionScotland	1.133	0.158	0.788	0.431	0.831	1.544
RegionSouth East	0.982	0.133	-0.134	0.894	0.757	1.274
RegionSouth West	0.911	0.148	-0.630	0.528	0.681	1.218
RegionWales	0.663	0.194	-2.120	0.034	0.453	0.969	*
RegionWest Midlands	0.935	0.145	-0.464	0.643	0.703	1.243
RegionYorkshire and the Humber	0.927	0.149	-0.508	0.612	0.692	1.242
BORNUK_binaryNot born in UK	0.900	0.133	-0.789	0.430	0.694	1.168
BORNUK_binaryPrefer not to say	1.889	0.429	1.482	0.138	0.815	4.381
High
(Intercept)	0.506	0.156	-4.378	0.000	0.373	0.686	***
Age	1.011	0.002	4.480	0.000	1.006	1.015	***
outsourcing_statusOutsourced	0.673	0.092	-4.325	0.000	0.562	0.805	***
Ethnicity_collapsed_disaggregatedIrish	0.609	0.313	-1.585	0.113	0.330	1.125
Ethnicity_collapsed_disaggregatedGypsy or Irish Traveller	0.948	1.276	-0.041	0.967	0.078	11.573
Ethnicity_collapsed_disaggregatedRoma	0.000	0.199	-74.594	0.000	0.000	0.000	***
Ethnicity_collapsed_disaggregatedAny other White background	1.144	0.178	0.755	0.451	0.807	1.622
Ethnicity_collapsed_disaggregatedWhite and Black Caribbean	0.858	0.383	-0.399	0.690	0.405	1.819
Ethnicity_collapsed_disaggregatedWhite and Black African	0.766	0.378	-0.704	0.481	0.366	1.607
Ethnicity_collapsed_disaggregatedWhite and Asian	1.346	0.331	0.897	0.370	0.703	2.575
Ethnicity_collapsed_disaggregatedAny other Mixed / Multiple ethnic background	0.939	0.340	-0.185	0.853	0.482	1.828
Ethnicity_collapsed_disaggregatedIndian	1.130	0.200	0.613	0.540	0.764	1.672
Ethnicity_collapsed_disaggregatedPakistani	0.478	0.293	-2.518	0.012	0.269	0.849	*
Ethnicity_collapsed_disaggregatedBangladeshi	0.881	0.412	-0.307	0.759	0.393	1.977
Ethnicity_collapsed_disaggregatedChinese	1.143	0.305	0.439	0.661	0.629	2.078
Ethnicity_collapsed_disaggregatedAny other Asian background	0.665	0.359	-1.138	0.255	0.329	1.343
Ethnicity_collapsed_disaggregatedAfrican	0.659	0.198	-2.106	0.035	0.447	0.972	*
Ethnicity_collapsed_disaggregatedCaribbean	1.279	0.292	0.843	0.399	0.722	2.266
Ethnicity_collapsed_disaggregatedAny other Black, Black British, or Caribbean background	0.753	0.425	-0.668	0.504	0.327	1.731
Ethnicity_collapsed_disaggregatedArab	2.159	0.587	1.312	0.189	0.684	6.817
Ethnicity_collapsed_disaggregatedAny other ethnic group	1.929	0.541	1.216	0.224	0.669	5.567
Ethnicity_collapsed_disaggregatedDon’t think of myself as any of these	0.000	0.194	-74.405	0.000	0.000	0.000	***
Ethnicity_collapsed_disaggregatedPrefer not to say	1.025	0.632	0.038	0.969	0.297	3.538
GenderFemale	0.494	0.065	-10.886	0.000	0.435	0.561	***
GenderOther	1.112	0.876	0.122	0.903	0.200	6.192
GenderPrefer not to say	0.968	0.714	-0.046	0.963	0.239	3.919
Has_DegreeNo	0.300	0.070	-17.209	0.000	0.262	0.344	***
Has_DegreeDon't know	0.494	0.354	-1.988	0.047	0.247	0.990	*
RegionEast Midlands	1.606	0.157	3.018	0.003	1.181	2.185	**
RegionEast of England	1.837	0.160	3.798	0.000	1.342	2.515	***
RegionNorth East	1.547	0.190	2.297	0.022	1.066	2.244	*
RegionNorth West	1.509	0.144	2.858	0.004	1.138	2.000	**
RegionNorthern Ireland	1.713	0.241	2.233	0.026	1.068	2.749	*
RegionScotland	0.995	0.162	-0.028	0.978	0.725	1.366
RegionSouth East	1.415	0.140	2.487	0.013	1.076	1.861	*
RegionSouth West	1.226	0.156	1.305	0.192	0.903	1.665
RegionWales	1.228	0.178	1.150	0.250	0.866	1.741
RegionWest Midlands	1.455	0.154	2.431	0.015	1.075	1.970	*
RegionYorkshire and the Humber	1.333	0.160	1.791	0.073	0.973	1.826
BORNUK_binaryNot born in UK	0.766	0.129	-2.063	0.039	0.595	0.987	*
BORNUK_binaryPrefer not to say	1.428	0.528	0.675	0.500	0.507	4.023

3.4.3.5 Ethnicity-migration interaction

We next explored whether there was an interaction between ethnicity and migration in predicting outsourcing using generalised linear models by adding an interaction effect to the model predicting outsourcing above so that the model formula is:

\[ Outsourcing = Ethnicity + Age + Gender + Educaton + Region + Migration + Ethnicity:Migration \]

where Ethnicity:Migration represents the interaction term.

We did this twice: first for the aggregated eight-level ethnicity variable, and then for the disaggregated 21-level variable.

3.4.3.5.1 Ethnicity 9

A model including the ethnicity:migration interaction term had significantly improved fit compared to a model without the interaction term, F(13, 9771) = 33.88, p < .001. The table below shows the model coefficients.

	Outsourcing
Predictors	Odds Ratios	CI	p
Intercept	0.62	0.47 – 0.82	0.001
Migration: Not born in the UK	2.16	1.60 – 2.91	<0.001
Migration: Prefer not to say	1.88	0.94 – 3.79	0.076
Ethnicity: Arab/British Arab	1.86	0.39 – 8.76	0.435
Ethnicity: Asian/Asian British	1.48	1.09 – 2.00	0.011
Ethnicity: Black/African/Caribbean/Black British	1.53	1.00 – 2.35	0.049
Ethnicity: Don't think of myself as any of these	5.49	1.00 – 30.12	0.050
Ethnicity: Mixed/Multiple ethnic group	1.16	0.81 – 1.67	0.406
Ethnicity: Other ethnic group	3.35	0.65 – 17.23	0.147
Ethnicity: Prefer not to say	1.99	0.44 – 8.97	0.369
Ethnicity: White other	1.06	0.64 – 1.74	0.825
Age	0.98	0.97 – 0.98	<0.001
Gender: Female	0.69	0.61 – 0.79	<0.001
Gender: Other	0.88	0.22 – 3.52	0.857
Gender: Prefer not to say	0.75	0.23 – 2.45	0.636
Education: Don't have degree	1.06	0.94 – 1.21	0.341
Education: Don't know	1.24	0.70 – 2.20	0.467
Region: East Midlands	0.95	0.72 – 1.26	0.744
Region: East of England	0.59	0.44 – 0.80	0.001
Region: North East	0.64	0.44 – 0.91	0.015
Region: North West	0.85	0.66 – 1.10	0.209
Region: Northern Ireland	0.69	0.44 – 1.08	0.106
Region: Scotland	0.69	0.51 – 0.94	0.020
Region: South East	0.62	0.49 – 0.79	<0.001
Region: South West	0.69	0.52 – 0.91	0.009
Region: Wales	0.92	0.67 – 1.26	0.597
Region: West Midlands	0.83	0.64 – 1.08	0.159
Region: Yorkshire and the Humber	0.68	0.52 – 0.90	0.007
Interaction: Not born in UK x Arab/Arab British	0.86	0.12 – 6.12	0.881
Interaction: Not born in UK x Asian/Asian British	0.52	0.32 – 0.87	0.012
Interaction: Prefer not to say x Asian/Asian British	0.26	0.05 – 1.27	0.096
Interaction: Not born in UK x Black/African/Caribbean/Black British	0.66	0.37 – 1.18	0.161
Interaction: Prefer not to say x Black/African/Caribbean/Black British	0.97	0.23 – 3.99	0.964
Interaction: Not born in UK x Don't think of myself as any of these	0.13	0.01 – 1.80	0.128
Interaction: Not born in UK x Mixed/Multiple ethnic group	1.27	0.60 – 2.70	0.530
Interaction: Prefer not to say x Mixed/Multiple ethnic group	0.39	0.03 – 4.64	0.453
Interaction: Not born in UK x Other ethnic group	0.00	0.00 – 0.00	<0.001
Interaction: Not born in UK x Prefer not to say	0.49	0.04 – 5.99	0.577
Interaction: Prefer not to say x Prefer not to say	0.00	0.00 – 0.00	<0.001
Interaction: Not born in UK x White other	0.53	0.28 – 1.01	0.053
Interaction: Prefer not to say x White other	0.59	0.10 – 3.61	0.564
Observations	9812

3.4.3.5.1.1 Post-hoc

We explored the interaction effect using targeted contrasts comparing

1. The effect of each ethnicity versus “White British” within each level of migration.
2. The effect of “Not born in UK” versus “Born in UK” within each level of ethnicity

Contrasts were calculated using survey::svycontrast() and p values were adjusted for multiple comparisons using the FDR method (Benjamini and Hochberg, 1995). Results were only considered where the sample for the contrast was greater than 10.

Exploring the effect of each ethnicity versus “White British” within each level of migration, we found that, among people not born in the UK, White other workers were 0.56 times as likely (i.e., 44% less likely) to be outsourced than “White British” people.

No differences by ethnicity were observed among people born in the UK.

Examining the effect of “Not born in UK” versus “Born in UK” within each ethnicity, we found

• among “White British”, workers not born in the UK are 2.16 times more likely to be outsourced than workers born in the UK.
• among people of Mixed/multiple ethnic groups, workers not born in UK are 2.74 times more likely to be outsourced than workers born in the UK.

No significant differences between people born and not born in the UK were observed for any other ethnicities. The figure below shows these effects.

3.4.3.5.2 Ethnicity 21

A model including the ethnicity:migration interaction term had significantly improved fit compared to a model without the interaction term, F(32, 9740) = 64.66, p < .001. The table below shows the model coefficients.

Note: Migration x Roma not estimable as model matrix rank deficient

	Outsourcing
Predictors	Odds Ratios	CI	p
Intercept	0.63	0.47 – 0.83	0.001
Migration: Not born in the UK	2.15	1.59 – 2.90	<0.001
Migration: Prefer not to say	1.89	0.94 – 3.79	0.075
Ethnicity: Irish	0.93	0.44 – 1.96	0.846
Ethnicity: Gypsy or Irish Traveller	1.67	0.31 – 8.85	0.547
Ethnicity: Roma	0.81	0.19 – 3.49	0.775
Ethnicity: Any other White background	1.11	0.55 – 2.26	0.769
Ethnicity: White and Black Caribbean	0.53	0.27 – 1.04	0.066
Ethnicity: White and Black African	3.38	1.68 – 6.82	0.001
Ethnicity: White and Asian	0.90	0.37 – 2.19	0.817
Ethnicity: Any other Mixed/Multiple ethnic background	1.87	0.96 – 3.65	0.067
Ethnicity: Indian	1.32	0.80 – 2.19	0.280
Ethnicity: Pakistani	2.67	1.68 – 4.25	<0.001
Ethnicity: Bangladeshi	1.81	0.84 – 3.86	0.128
Ethnicity: Chinese	0.53	0.16 – 1.74	0.299
Ethnicity: Any other Asian background	1.06	0.36 – 3.11	0.916
Ethnicity: African	1.52	0.87 – 2.64	0.140
Ethnicity: Caribbean	1.12	0.49 – 2.53	0.787
Ethnicity: Any other Black, Black British, or Caribbean background	2.60	1.05 – 6.41	0.038
Ethnicity: Arab	1.85	0.39 – 8.69	0.435
Ethnicity: Any other ethnic group	3.34	0.65 – 17.32	0.150
Ethnicity: Don't think of myself as any of these	5.43	1.00 – 29.50	0.050
Ethnicity: Prefer not to say	1.99	0.44 – 8.97	0.370
Age	0.98	0.97 – 0.98	<0.001
Gender: Female	0.69	0.61 – 0.79	<0.001
Gender: Other	0.87	0.22 – 3.44	0.846
Gender: Prefer not to say	0.77	0.24 – 2.50	0.662
Education: Don't have degree	1.06	0.93 – 1.20	0.405
Education: Don't know	1.21	0.67 – 2.18	0.519
Region: East Midlands	0.94	0.71 – 1.25	0.692
Region: East of England	0.59	0.44 – 0.80	0.001
Region: North East	0.63	0.44 – 0.91	0.014
Region: North West	0.84	0.65 – 1.08	0.179
Region: Northern Ireland	0.71	0.44 – 1.14	0.153
Region: Scotland	0.69	0.50 – 0.93	0.017
Region: South East	0.62	0.49 – 0.79	<0.001
Region: South West	0.68	0.51 – 0.90	0.007
Region: Wales	0.90	0.65 – 1.24	0.524
Region: West Midlands	0.80	0.61 – 1.04	0.093
Region: Yorkshire and the Humber	0.67	0.51 – 0.88	0.004
Interaction: Not born in UK x Irish	0.28	0.07 – 1.19	0.085
Interaction: Prefer not to say x Irish	1.31	0.09 – 18.48	0.842
Interaction: Not born in UK x Gypsy or Irish Traveller	0.00	0.00 – 0.00	<0.001
Interaction: Not born in UK x Any other White background	0.52	0.23 – 1.18	0.118
Interaction: Prefer not to say x Any other White background	0.38	0.04 – 3.92	0.413
Interaction: Not born in UK x White and Black Caribbean	0.00	0.00 – 0.00	<0.001
Interaction: Prefer not to say x White and Black Caribbean	0.00	0.00 – 0.00	<0.001
Interaction: Not born in UK x White and Black African	0.46	0.14 – 1.52	0.203
Interaction: Prefer not to say x White and Black African	0.00	0.00 – 0.00	<0.001
Interaction: Not born in UK x White and Asian	2.76	0.48 – 15.70	0.253
Interaction: Not born in UK x Any other Mixed/Multiple ethnic background	0.69	0.21 – 2.26	0.541
Interaction: Prefer not to say x Any other Mixed/Multiple ethnic background	1.10	0.03 – 41.99	0.960
Interaction: Not born in UK x Indian	0.55	0.26 – 1.17	0.120
Interaction: Prefer not to say x Indian	0.38	0.04 – 3.31	0.380
Interaction: Not born in UK x Pakistani	0.43	0.17 – 1.09	0.074
Interaction: Prefer not to say x Pakistani	0.00	0.00 – 0.00	<0.001
Interaction: Not born in UK x Bangladeshi	0.38	0.10 – 1.48	0.162
Interaction: Prefer not to say x Bangladeshi	0.30	0.03 – 2.99	0.305
Interaction: Not born in UK x Chinese	0.88	0.21 – 3.70	0.864
Interaction: Not born in UK x Any other Asian background	0.90	0.26 – 3.13	0.864
Interaction: Prefer not to say x Any other Asian background	0.00	0.00 – 0.00	<0.001
Interaction: Not born in UK x African	0.69	0.35 – 1.36	0.278
Interaction: Prefer not to say x African	0.89	0.19 – 4.18	0.886
Interaction: Not born in UK x Caribbean	0.84	0.14 – 5.19	0.851
Interaction: Prefer not to say x Caribbean	8523821.18	897250.55 – 80975740.17	<0.001
Interaction: Not born in UK x Any other Black, Black British, or Caribbean background	0.25	0.05 – 1.15	0.075
Interaction: Prefer not to say x Any other Black, Black British, or Caribbean background	0.00	0.00 – 0.00	<0.001
Interaction: Not born in UK x Arab	0.86	0.12 – 6.11	0.880
Interaction: Not born in UK x Any other ethnic group	0.00	0.00 – 0.00	<0.001
Interaction: Not born in UK x Don't think of myself as any of these	0.13	0.01 – 1.82	0.129
Interaction: Not born in UK x Prefer not to say	0.49	0.04 – 5.98	0.579
Interaction: Prefer not to say x Prefer not to say	0.00	0.00 – 0.00	<0.001
Observations	9812

3.4.3.5.2.1 Post-hoc

We explored the interaction effect using targeted contrasts comparing

1. The effect of each ethnicity versus “English / Welsh / Scottish / Northern Irish / British” within each level of migration.
2. The effect of “Not born in UK” versus “Born in UK” within each level of ethnicity

Contrasts were calculated using survey::svycontrast() and p values were adjusted for multiple comparisons using the FDR method (Benjamini and Hochberg, 1995) and results were only considered where the sample for the contrast was greater than 10.

Exploring the effect of each ethnicity versus “English / Welsh / Scottish / Northern Irish / British” within each level of migration, we found that, among people born in the UK

• White and Black African people were 3.38 times more likely to be outsourced than “English / Welsh / Scottish / Northern Irish / British” people.
• Pakistani people were 2.67 times more likely to be outsourced than “English / Welsh / Scottish / Northern Irish / British” people.

Among people not born in the UK, no significant differences between ethnicities were observed. The figure below shows the effects for “English / Welsh / Scottish / Northern Irish / British”, “White and Black African”, and Pakistani respondents.

Examining the effect of “Not born in UK” versus “Born in UK” within each level of ethnicity, we found that among “English / Welsh / Scottish / Northern Irish / British”, workers not born in the UK are 2.15 times more likely to be outsourced than workers born in the UK.

No significant differences between people born and not born in the UK were observed for any other ethnicities. The figure below shows these effects.

3.4.3.6 Ethnicity-outsourced interaction

A generalised linear model was constructed to test whether the interaction between ethnicity and outsourcing predicted whether a person had a low income.

\[ Income Group = Outsourcing + Ethnicity + Age + Gender + Education + Region + Migration + Outsourcing:Ethnicity \]

As in preceding sections, we constructed three models; one for the binary ethnicity variable, one for the eight-level ethnicity variable, and one for the 21-level ethnicity variable.

3.4.3.6.1 Ethnicity binary

A saturated model including the interaction term was not a significantly better fit to the data than an intercept-only model, X^2(2) = 0.9400127, p = 0.625. The table below shows the model coefficients of the simpler model without the interaction term.

income_group	estimate	std.error	statistic	p.value	conf.low	conf.high	sig
Low
(Intercept)	0.136	0.171	-11.689	0.000	0.097	0.190	***
outsourcing_statusOutsourced	1.358	0.088	3.463	0.001	1.142	1.614	***
Ethnicity_binaryNon-White	1.075	0.114	0.633	0.526	0.859	1.345
Age	1.007	0.003	2.652	0.008	1.002	1.013	**
GenderFemale	2.755	0.071	14.280	0.000	2.397	3.166	***
GenderOther	3.948	0.664	2.068	0.039	1.074	14.508	*
GenderPrefer not to say	2.062	0.844	0.858	0.391	0.394	10.786
Has_DegreeNo	1.920	0.067	9.779	0.000	1.685	2.189	***
Has_DegreeDon't know	3.229	0.309	3.796	0.000	1.763	5.916	***
RegionEast Midlands	1.077	0.152	0.487	0.627	0.799	1.452
RegionEast of England	1.104	0.158	0.625	0.532	0.810	1.503
RegionNorth East	0.822	0.191	-1.027	0.304	0.565	1.195
RegionNorth West	0.737	0.145	-2.108	0.035	0.555	0.979	*
RegionNorthern Ireland	1.157	0.207	0.704	0.481	0.771	1.738
RegionScotland	1.095	0.157	0.580	0.562	0.805	1.490
RegionSouth East	0.973	0.132	-0.207	0.836	0.751	1.261
RegionSouth West	0.885	0.147	-0.827	0.408	0.663	1.182
RegionWales	0.650	0.192	-2.240	0.025	0.446	0.948	*
RegionWest Midlands	0.906	0.144	-0.687	0.492	0.683	1.202
RegionYorkshire and the Humber	0.907	0.149	-0.659	0.510	0.678	1.213
BORNUK_labelledWithin the last year	1.374	0.247	1.286	0.199	0.846	2.230
BORNUK_labelledWithin the last 3 years	0.901	0.244	-0.425	0.671	0.558	1.455
BORNUK_labelledWithin the last 5 years	1.000	0.267	-0.001	0.999	0.593	1.686
BORNUK_labelledWithin the last 10 years	0.925	0.221	-0.353	0.724	0.600	1.425
BORNUK_labelledWithin the last 15 years	0.911	0.254	-0.367	0.714	0.554	1.498
BORNUK_labelledWithin the last 20 years	0.834	0.244	-0.743	0.458	0.517	1.346
BORNUK_labelledWithin the last 30 years	0.622	0.425	-1.116	0.264	0.270	1.432
BORNUK_labelledMore than 30 years ago	0.894	0.264	-0.425	0.671	0.533	1.500
BORNUK_labelledPrefer not to say	2.226	0.429	1.864	0.062	0.960	5.163
High
(Intercept)	0.516	0.156	-4.239	0.000	0.380	0.700	***
outsourcing_statusOutsourced	0.661	0.092	-4.504	0.000	0.552	0.792	***
Ethnicity_binaryNon-White	0.985	0.108	-0.140	0.889	0.797	1.218
Age	1.010	0.002	4.257	0.000	1.005	1.015	***
GenderFemale	0.497	0.065	-10.814	0.000	0.438	0.564	***
GenderOther	1.120	0.878	0.129	0.897	0.200	6.263
GenderPrefer not to say	0.935	0.696	-0.096	0.923	0.239	3.662
Has_DegreeNo	0.301	0.070	-17.176	0.000	0.262	0.345	***
Has_DegreeDon't know	0.499	0.348	-2.000	0.046	0.252	0.986	*
RegionEast Midlands	1.596	0.156	2.986	0.003	1.174	2.168	**
RegionEast of England	1.839	0.159	3.832	0.000	1.347	2.511	***
RegionNorth East	1.542	0.188	2.307	0.021	1.067	2.227	*
RegionNorth West	1.484	0.143	2.761	0.006	1.121	1.965	**
RegionNorthern Ireland	1.561	0.231	1.931	0.054	0.993	2.453
RegionScotland	0.994	0.162	-0.040	0.968	0.724	1.364
RegionSouth East	1.411	0.139	2.469	0.014	1.074	1.854	*
RegionSouth West	1.224	0.156	1.298	0.194	0.902	1.660
RegionWales	1.226	0.178	1.149	0.251	0.866	1.738
RegionWest Midlands	1.430	0.154	2.322	0.020	1.057	1.935	*
RegionYorkshire and the Humber	1.306	0.159	1.683	0.092	0.957	1.783
BORNUK_labelledWithin the last year	0.323	0.337	-3.358	0.001	0.167	0.624	***
BORNUK_labelledWithin the last 3 years	0.488	0.270	-2.659	0.008	0.288	0.828	**
BORNUK_labelledWithin the last 5 years	0.760	0.243	-1.127	0.260	0.472	1.224
BORNUK_labelledWithin the last 10 years	0.754	0.200	-1.414	0.157	0.510	1.115
BORNUK_labelledWithin the last 15 years	1.011	0.233	0.045	0.964	0.640	1.595
BORNUK_labelledWithin the last 20 years	0.999	0.241	-0.006	0.996	0.622	1.602
BORNUK_labelledWithin the last 30 years	0.701	0.320	-1.111	0.266	0.374	1.312
BORNUK_labelledMore than 30 years ago	1.078	0.221	0.341	0.733	0.699	1.665
BORNUK_labelledPrefer not to say	1.412	0.539	0.640	0.522	0.491	4.062

3.4.3.6.2 Ethnicity 9

A model including the ethnicity:outsourcing interaction term significantly improved model fit compared to a model without the interaction term, X^2(12) = 238.2925359, p < .001. The table below shows the model coefficients.

income_group	estimate	std.error	statistic	p.value	conf.low	conf.high	sig
Mid
(Intercept)	0.132	0.172	-11.768	0.000	0.094	0.184	***
outsourcing_statusOutsourced	1.428	0.102	3.480	0.001	1.168	1.746	***
Ethnicity_collapsedArab/British Arab	1.844	0.827	0.740	0.459	0.365	9.326
Ethnicity_collapsedAsian/Asian British	1.076	0.179	0.410	0.682	0.758	1.527
Ethnicity_collapsedBlack/African/Caribbean/Black British	1.349	0.196	1.526	0.127	0.918	1.982
Ethnicity_collapsedMixed/Multiple ethnic group	1.066	0.213	0.299	0.765	0.702	1.618
Ethnicity_collapsedOther ethnic group	0.000	0.271	-51.997	0.000	0.000	0.000	***
Ethnicity_collapsedWhite other	1.128	0.193	0.626	0.531	0.773	1.647
Age	1.007	0.003	2.762	0.006	1.002	1.013	**
GenderFemale	2.789	0.071	14.431	0.000	2.426	3.205	***
GenderOther	4.090	0.671	2.100	0.036	1.098	15.232	*
GenderPrefer not to say	2.078	0.845	0.866	0.387	0.397	10.880
Has_DegreeNo	1.923	0.067	9.813	0.000	1.688	2.192	***
Has_DegreeDon't know	3.215	0.311	3.754	0.000	1.747	5.915	***
RegionEast Midlands	1.076	0.153	0.479	0.632	0.798	1.452
RegionEast of England	1.098	0.158	0.592	0.554	0.805	1.497
RegionNorth East	0.827	0.192	-0.992	0.321	0.567	1.204
RegionNorth West	0.731	0.145	-2.156	0.031	0.550	0.972	*
RegionNorthern Ireland	1.131	0.206	0.595	0.552	0.754	1.695
RegionScotland	1.105	0.158	0.630	0.528	0.811	1.506
RegionSouth East	0.969	0.133	-0.233	0.816	0.746	1.259
RegionSouth West	0.880	0.148	-0.868	0.385	0.659	1.175
RegionWales	0.649	0.192	-2.244	0.025	0.445	0.947	*
RegionWest Midlands	0.901	0.145	-0.722	0.470	0.678	1.196
RegionYorkshire and the Humber	0.913	0.149	-0.611	0.541	0.682	1.223
BORNUK_labelledWithin the last year	1.461	0.266	1.426	0.154	0.868	2.459
BORNUK_labelledWithin the last 3 years	0.898	0.245	-0.439	0.660	0.555	1.452
BORNUK_labelledWithin the last 5 years	0.953	0.278	-0.174	0.862	0.553	1.642
BORNUK_labelledWithin the last 10 years	0.918	0.251	-0.341	0.733	0.561	1.502
BORNUK_labelledWithin the last 15 years	0.895	0.274	-0.404	0.686	0.523	1.531
BORNUK_labelledWithin the last 20 years	0.837	0.262	-0.679	0.497	0.501	1.398
BORNUK_labelledWithin the last 30 years	0.573	0.418	-1.331	0.183	0.253	1.301
BORNUK_labelledMore than 30 years ago	0.897	0.266	-0.411	0.681	0.532	1.510
BORNUK_labelledPrefer not to say	2.270	0.443	1.849	0.064	0.952	5.411
outsourcing_statusOutsourced:Ethnicity_collapsedArab/British Arab	0.690	1.414	-0.263	0.793	0.043	11.020
outsourcing_statusOutsourced:Ethnicity_collapsedAsian/Asian British	0.824	0.297	-0.654	0.513	0.461	1.473
outsourcing_statusOutsourced:Ethnicity_collapsedBlack/African/Caribbean/Black British	0.380	0.367	-2.637	0.008	0.185	0.780	**
outsourcing_statusOutsourced:Ethnicity_collapsedMixed/Multiple ethnic group	2.858	0.418	2.515	0.012	1.261	6.480	*
outsourcing_statusOutsourced:Ethnicity_collapsedOther ethnic group	7934713.109	1.500	10.591	0.000	419527.941	150072655.492	***
outsourcing_statusOutsourced:Ethnicity_collapsedWhite other	0.719	0.339	-0.972	0.331	0.370	1.398
High
(Intercept)	0.522	0.158	-4.113	0.000	0.383	0.711	***
outsourcing_statusOutsourced	0.638	0.109	-4.134	0.000	0.515	0.789	***
Ethnicity_collapsedArab/British Arab	2.984	0.773	1.414	0.157	0.656	13.581
Ethnicity_collapsedAsian/Asian British	0.917	0.161	-0.538	0.591	0.669	1.257
Ethnicity_collapsedBlack/African/Caribbean/Black British	0.850	0.192	-0.850	0.395	0.583	1.237
Ethnicity_collapsedMixed/Multiple ethnic group	0.850	0.204	-0.795	0.427	0.570	1.268
Ethnicity_collapsedOther ethnic group	1.749	0.544	1.027	0.304	0.602	5.082
Ethnicity_collapsedWhite other	0.931	0.163	-0.435	0.663	0.676	1.283
Age	1.010	0.002	4.267	0.000	1.005	1.015	***
GenderFemale	0.497	0.065	-10.800	0.000	0.437	0.564	***
GenderOther	1.125	0.882	0.133	0.894	0.200	6.336
GenderPrefer not to say	0.938	0.699	-0.092	0.927	0.238	3.689
Has_DegreeNo	0.301	0.070	-17.210	0.000	0.262	0.345	***
Has_DegreeDon't know	0.501	0.348	-1.988	0.047	0.253	0.990	*
RegionEast Midlands	1.599	0.157	2.986	0.003	1.175	2.177	**
RegionEast of England	1.836	0.159	3.811	0.000	1.343	2.509	***
RegionNorth East	1.535	0.190	2.257	0.024	1.058	2.226	*
RegionNorth West	1.473	0.144	2.688	0.007	1.111	1.954	**
RegionNorthern Ireland	1.580	0.233	1.962	0.050	1.000	2.494	*
RegionScotland	0.988	0.163	-0.075	0.940	0.718	1.360
RegionSouth East	1.394	0.140	2.373	0.018	1.060	1.834	*
RegionSouth West	1.207	0.157	1.204	0.229	0.888	1.641
RegionWales	1.214	0.178	1.087	0.277	0.856	1.721
RegionWest Midlands	1.428	0.155	2.301	0.021	1.054	1.934	*
RegionYorkshire and the Humber	1.296	0.161	1.616	0.106	0.946	1.776
BORNUK_labelledWithin the last year	0.333	0.348	-3.164	0.002	0.169	0.658	**
BORNUK_labelledWithin the last 3 years	0.509	0.270	-2.503	0.012	0.300	0.864	*
BORNUK_labelledWithin the last 5 years	0.757	0.254	-1.098	0.272	0.460	1.245
BORNUK_labelledWithin the last 10 years	0.774	0.218	-1.176	0.240	0.505	1.186
BORNUK_labelledWithin the last 15 years	1.034	0.250	0.132	0.895	0.634	1.686
BORNUK_labelledWithin the last 20 years	1.052	0.250	0.204	0.838	0.645	1.717
BORNUK_labelledWithin the last 30 years	0.719	0.329	-1.001	0.317	0.377	1.371
BORNUK_labelledMore than 30 years ago	1.080	0.222	0.346	0.729	0.699	1.668
BORNUK_labelledPrefer not to say	1.450	0.542	0.685	0.493	0.501	4.192
outsourcing_statusOutsourced:Ethnicity_collapsedArab/British Arab	0.340	1.419	-0.760	0.447	0.021	5.488
outsourcing_statusOutsourced:Ethnicity_collapsedAsian/Asian British	1.079	0.299	0.254	0.800	0.600	1.939
outsourcing_statusOutsourced:Ethnicity_collapsedBlack/African/Caribbean/Black British	1.305	0.306	0.870	0.384	0.716	2.378
outsourcing_statusOutsourced:Ethnicity_collapsedMixed/Multiple ethnic group	1.998	0.442	1.564	0.118	0.839	4.755
outsourcing_statusOutsourced:Ethnicity_collapsedOther ethnic group	1.932	1.425	0.462	0.644	0.118	31.562
outsourcing_statusOutsourced:Ethnicity_collapsedWhite other	1.004	0.397	0.009	0.993	0.461	2.184

We explored the interaction effect using targeted contrasts comparing

1. The effect of outsourcing within each level of ethnicity
2. The effect of each ethnicity versus “White British” within each level of outsourcing

We do not consider contrasts for which any cell count is less than 10.

3.4.3.6.2.1 Post-hoc: Outsourcing within ethnicity

• White British workers are 1.2290804 times as likely to be in the Mid group if they are outsourced compared to not-outsourced
• White British workers are 1.8204336 times as likely to be in the Low group if they are outsourced compared to not-outsourced
• White British workers are 0.6683549 times as likely to be in the High group if they are outsourced compared to not-outsourced
• Mixed/Multiple ethnic group workers are 6.0698533 times as likely to be in the Low group if they are outsourced compared to not-outsourced (note large confidence interval for this effect - see plot)

In essence, a White British person is more likely to be in the low or mid income group, and less likely to be in the high income group, if they are outsourced compared to not-outsourced.

3.4.3.6.2.2 Post-hoc: Ethnicity within outsourcing

Comparing ethnicities within outsourcing status revealed no significant contrasts.

The plot below of predicted probabilities suggests that non-White ethnicities are not more or less likely to be in the low income group regardless of the outsourcing status, compared to White British. That is, the lack of an effect of outsourcing status on low income group membership does not appear to be attributable to a higher likelihood generally of marginalised ethnicities being low paid.

3.4.3.6.3 Ethnicity 21

A model including the ethnicity:outsourcing interaction term significantly improved model fit compared to a model without the interaction term, X^2(38) = 2276.50414, p < .001. The table below shows the model coefficients.

income_group	estimate	std.error	statistic	p.value	conf.low	conf.high	sig
Mid
(Intercept)	0.130	0.173	-11.796	0.000	0.093	0.182	***
outsourcing_statusOutsourced	1.428	0.102	3.479	0.001	1.168	1.746	***
Ethnicity_collapsed_disaggregatedIrish	1.629	0.300	1.628	0.103	0.905	2.932
Ethnicity_collapsed_disaggregatedGypsy or Irish Traveller	0.295	1.073	-1.137	0.256	0.036	2.420
Ethnicity_collapsed_disaggregatedRoma	0.000	0.308	-51.946	0.000	0.000	0.000	***
Ethnicity_collapsed_disaggregatedAny other White background	1.090	0.231	0.372	0.710	0.692	1.716
Ethnicity_collapsed_disaggregatedWhite and Black Caribbean	1.097	0.377	0.245	0.806	0.524	2.297
Ethnicity_collapsed_disaggregatedWhite and Black African	0.711	0.585	-0.583	0.560	0.226	2.236
Ethnicity_collapsed_disaggregatedWhite and Asian	1.085	0.381	0.215	0.830	0.514	2.291
Ethnicity_collapsed_disaggregatedAny other Mixed / Multiple ethnic background	1.235	0.385	0.549	0.583	0.581	2.629
Ethnicity_collapsed_disaggregatedIndian	0.895	0.269	-0.415	0.678	0.528	1.514
Ethnicity_collapsed_disaggregatedPakistani	1.241	0.314	0.690	0.490	0.672	2.295
Ethnicity_collapsed_disaggregatedBangladeshi	1.491	0.449	0.889	0.374	0.618	3.597
Ethnicity_collapsed_disaggregatedChinese	0.631	0.408	-1.128	0.259	0.283	1.404
Ethnicity_collapsed_disaggregatedAny other Asian background	1.628	0.405	1.205	0.228	0.737	3.599
Ethnicity_collapsed_disaggregatedAfrican	1.297	0.243	1.070	0.285	0.806	2.089
Ethnicity_collapsed_disaggregatedCaribbean	1.074	0.404	0.177	0.860	0.486	2.372
Ethnicity_collapsed_disaggregatedAny other Black, Black British, or Caribbean background	2.481	0.479	1.897	0.058	0.970	6.346
Ethnicity_collapsed_disaggregatedArab	1.804	0.832	0.710	0.478	0.354	9.208
Ethnicity_collapsed_disaggregatedAny other ethnic group	0.000	0.274	-55.213	0.000	0.000	0.000	***
Ethnicity_collapsed_disaggregatedDon’t think of myself as any of these	0.635	1.095	-0.415	0.678	0.074	5.432
Age	1.008	0.003	2.867	0.004	1.002	1.013	**
GenderFemale	2.783	0.071	14.373	0.000	2.421	3.200	***
GenderOther	4.045	0.668	2.093	0.036	1.093	14.972	*
GenderPrefer not to say	2.010	0.847	0.825	0.410	0.382	10.571
Has_DegreeNo	1.931	0.067	9.822	0.000	1.694	2.202	***
Has_DegreeDon't know	3.200	0.306	3.800	0.000	1.756	5.830	***
RegionEast Midlands	1.098	0.153	0.609	0.543	0.813	1.482
RegionEast of England	1.090	0.158	0.544	0.586	0.799	1.487
RegionNorth East	0.816	0.193	-1.053	0.292	0.558	1.192
RegionNorth West	0.727	0.146	-2.174	0.030	0.546	0.969	*
RegionNorthern Ireland	1.071	0.214	0.321	0.749	0.704	1.629
RegionScotland	1.112	0.158	0.673	0.501	0.816	1.517
RegionSouth East	0.965	0.134	-0.264	0.792	0.742	1.255
RegionSouth West	0.878	0.149	-0.876	0.381	0.656	1.175
RegionWales	0.645	0.196	-2.245	0.025	0.439	0.946	*
RegionWest Midlands	0.896	0.146	-0.748	0.455	0.673	1.194
RegionYorkshire and the Humber	0.904	0.150	-0.675	0.500	0.673	1.213
BORNUK_labelledWithin the last year	1.537	0.277	1.550	0.121	0.892	2.647
BORNUK_labelledWithin the last 3 years	0.987	0.261	-0.051	0.960	0.592	1.645
BORNUK_labelledWithin the last 5 years	1.017	0.285	0.058	0.953	0.582	1.777
BORNUK_labelledWithin the last 10 years	0.895	0.255	-0.435	0.664	0.544	1.474
BORNUK_labelledWithin the last 15 years	0.886	0.286	-0.424	0.672	0.506	1.551
BORNUK_labelledWithin the last 20 years	0.865	0.264	-0.549	0.583	0.515	1.452
BORNUK_labelledWithin the last 30 years	0.545	0.454	-1.337	0.181	0.224	1.327
BORNUK_labelledMore than 30 years ago	0.870	0.275	-0.508	0.612	0.508	1.490
BORNUK_labelledPrefer not to say	2.168	0.435	1.778	0.075	0.924	5.088
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedIrish	0.273	0.867	-1.497	0.134	0.050	1.494
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedGypsy or Irish Traveller	0.000	1.527	-8.518	0.000	0.000	0.000	***
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedRoma	1.634	0.440	1.117	0.264	0.690	3.868
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other White background	0.827	0.369	-0.515	0.607	0.401	1.705
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedWhite and Black Caribbean	2.145	0.820	0.931	0.352	0.430	10.701
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedWhite and Black African	1.616	0.921	0.521	0.602	0.266	9.824
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedWhite and Asian	20.168	1.116	2.691	0.007	2.262	179.829	**
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other Mixed / Multiple ethnic background	3.586	0.769	1.661	0.097	0.795	16.184
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedIndian	0.768	0.480	-0.550	0.582	0.300	1.966
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedPakistani	1.066	0.502	0.127	0.899	0.399	2.849
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedBangladeshi	1.261	0.811	0.286	0.775	0.257	6.176
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedChinese	1.755	0.949	0.593	0.553	0.273	11.271
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other Asian background	0.255	0.705	-1.940	0.052	0.064	1.014
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAfrican	0.438	0.391	-2.107	0.035	0.203	0.944	*
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedCaribbean	0.576	1.066	-0.518	0.605	0.071	4.655
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other Black, Black British, or Caribbean background	0.044	1.165	-2.674	0.007	0.005	0.435	**
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedArab	0.701	1.405	-0.252	0.801	0.045	11.011
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other ethnic group	21564953.458	1.498	11.274	0.000	1144826.512	406216324.123	***
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedDon’t think of myself as any of these	0.296	1.581	-0.770	0.441	0.013	6.560
High
(Intercept)	0.519	0.159	-4.133	0.000	0.380	0.708	***
outsourcing_statusOutsourced	0.637	0.109	-4.143	0.000	0.514	0.788	***
Ethnicity_collapsed_disaggregatedIrish	0.652	0.323	-1.323	0.186	0.346	1.229
Ethnicity_collapsed_disaggregatedGypsy or Irish Traveller	0.000	0.271	-57.918	0.000	0.000	0.000	***
Ethnicity_collapsed_disaggregatedRoma	0.000	0.304	-51.196	0.000	0.000	0.000	***
Ethnicity_collapsed_disaggregatedAny other White background	1.110	0.190	0.550	0.583	0.765	1.612
Ethnicity_collapsed_disaggregatedWhite and Black Caribbean	0.936	0.395	-0.167	0.868	0.431	2.032
Ethnicity_collapsed_disaggregatedWhite and Black African	0.736	0.471	-0.649	0.516	0.292	1.855
Ethnicity_collapsed_disaggregatedWhite and Asian	0.998	0.357	-0.005	0.996	0.496	2.008
Ethnicity_collapsed_disaggregatedAny other Mixed / Multiple ethnic background	0.701	0.365	-0.971	0.331	0.343	1.435
Ethnicity_collapsed_disaggregatedIndian	1.209	0.229	0.828	0.408	0.771	1.895
Ethnicity_collapsed_disaggregatedPakistani	0.508	0.325	-2.083	0.037	0.269	0.961	*
Ethnicity_collapsed_disaggregatedBangladeshi	0.532	0.489	-1.291	0.197	0.204	1.387
Ethnicity_collapsed_disaggregatedChinese	0.990	0.334	-0.030	0.976	0.514	1.906
Ethnicity_collapsed_disaggregatedAny other Asian background	0.894	0.417	-0.267	0.789	0.395	2.027
Ethnicity_collapsed_disaggregatedAfrican	0.730	0.246	-1.278	0.201	0.450	1.183
Ethnicity_collapsed_disaggregatedCaribbean	1.326	0.327	0.864	0.388	0.699	2.516
Ethnicity_collapsed_disaggregatedAny other Black, Black British, or Caribbean background	0.729	0.538	-0.587	0.557	0.254	2.093
Ethnicity_collapsed_disaggregatedArab	2.997	0.773	1.419	0.156	0.658	13.644
Ethnicity_collapsed_disaggregatedAny other ethnic group	1.818	0.549	1.088	0.277	0.619	5.334
Ethnicity_collapsed_disaggregatedDon’t think of myself as any of these	0.000	0.292	-53.603	0.000	0.000	0.000	***
Age	1.010	0.002	4.100	0.000	1.005	1.015	***
GenderFemale	0.489	0.065	-11.013	0.000	0.430	0.555	***
GenderOther	1.105	0.882	0.114	0.909	0.196	6.226
GenderPrefer not to say	0.959	0.711	-0.058	0.953	0.238	3.862
Has_DegreeNo	0.298	0.070	-17.260	0.000	0.260	0.342	***
Has_DegreeDon't know	0.495	0.350	-2.014	0.044	0.249	0.981	*
RegionEast Midlands	1.646	0.158	3.157	0.002	1.208	2.243	**
RegionEast of England	1.888	0.161	3.956	0.000	1.378	2.587	***
RegionNorth East	1.577	0.190	2.400	0.016	1.087	2.287	*
RegionNorth West	1.532	0.146	2.921	0.003	1.151	2.040	**
RegionNorthern Ireland	1.761	0.241	2.344	0.019	1.097	2.825	*
RegionScotland	1.012	0.164	0.075	0.940	0.735	1.395
RegionSouth East	1.445	0.142	2.598	0.009	1.095	1.907	**
RegionSouth West	1.248	0.158	1.406	0.160	0.916	1.700
RegionWales	1.258	0.180	1.279	0.201	0.885	1.789
RegionWest Midlands	1.475	0.156	2.486	0.013	1.086	2.005	*
RegionYorkshire and the Humber	1.334	0.162	1.776	0.076	0.971	1.834
BORNUK_labelledWithin the last year	0.354	0.367	-2.830	0.005	0.173	0.727	**
BORNUK_labelledWithin the last 3 years	0.516	0.278	-2.381	0.017	0.299	0.890	*
BORNUK_labelledWithin the last 5 years	0.792	0.261	-0.893	0.372	0.476	1.320
BORNUK_labelledWithin the last 10 years	0.738	0.227	-1.341	0.180	0.473	1.151
BORNUK_labelledWithin the last 15 years	0.928	0.256	-0.291	0.771	0.562	1.534
BORNUK_labelledWithin the last 20 years	1.051	0.263	0.191	0.849	0.628	1.760
BORNUK_labelledWithin the last 30 years	0.672	0.331	-1.204	0.229	0.351	1.284
BORNUK_labelledMore than 30 years ago	1.012	0.229	0.051	0.960	0.646	1.583
BORNUK_labelledPrefer not to say	1.483	0.554	0.711	0.477	0.501	4.391
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedIrish	0.417	1.182	-0.739	0.460	0.041	4.231
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedGypsy or Irish Traveller	128775445.181	1.823	10.243	0.000	3614467.824	4587982543.925	***
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedRoma	0.522	0.428	-1.520	0.129	0.225	1.207
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other White background	0.901	0.412	-0.253	0.800	0.402	2.020
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedWhite and Black Caribbean	0.000	0.536	-26.297	0.000	0.000	0.000	***
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedWhite and Black African	1.455	0.762	0.492	0.623	0.327	6.481
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedWhite and Asian	14.464	1.228	2.176	0.030	1.303	160.512	*
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other Mixed / Multiple ethnic background	3.592	0.805	1.588	0.112	0.741	17.404
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedIndian	0.922	0.443	-0.182	0.855	0.387	2.198
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedPakistani	0.796	0.748	-0.305	0.761	0.184	3.450
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedBangladeshi	4.675	0.859	1.796	0.073	0.868	25.166
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedChinese	3.235	0.795	1.477	0.140	0.681	15.373
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other Asian background	0.395	0.794	-1.172	0.241	0.083	1.869
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAfrican	1.527	0.358	1.183	0.237	0.757	3.080
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedCaribbean	0.863	0.707	-0.208	0.835	0.216	3.450
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other Black, Black British, or Caribbean background	1.543	0.838	0.517	0.605	0.299	7.969
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedArab	0.357	1.402	-0.734	0.463	0.023	5.576
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedAny other ethnic group	1.866	1.429	0.437	0.662	0.113	30.695
outsourcing_statusOutsourced:Ethnicity_collapsed_disaggregatedDon’t think of myself as any of these	1.993	0.424	1.627	0.104	0.868	4.576

3.4.3.6.3.1 Post-hoc: Outsourcing within ethnicity

• White and Black Caribbean workers are 0.0000004 times as likely to be in the High group if they are outsourced compared to not-outsourced
• English / Welsh / Scottish / Northern Irish / British workers are 1.2271883 times as likely to be in the Mid group if they are outsourced compared to not-outsourced
• English / Welsh / Scottish / Northern Irish / British workers are 1.819074 times as likely to be in the Low group if they are outsourced compared to not-outsourced
• English / Welsh / Scottish / Northern Irish / British workers are 0.6632549 times as likely to be in the High group if they are outsourced compared to not-outsourced

3.4.3.6.3.2 Post-hoc: Ethnicity within outsourcing

Comparing ethnicities within outsourcing status,

• Among Not outsourced workers, people of Irish ethnicity are 2.7221653 times as likely to be in the Low group compared to English / Welsh / Scottish / Northern Irish / British people.

As for the aggregated model, there is no evidence to suggest that the lack of a difference for ethnic minorities is due to a higher likelihood of being low paid regardless of outsourcing status.

4 Analysis - Study 2

5 Study 2 Overview

Analysis from Study 2 appearing in [NAME OF REPORT] primarily employs a descriptive approach to understand the data. We conducted several cross-tabulations focusing on key demographic variables including Migration Status, Low Pay, and Ethnicity.

Due to the extensive number of variables examined, these cross-tabulations are not reproduced in this document. However, researchers can easily recreate these analyses by running the “Crosstabulations.qmd” script available in the GitHub repository associated with this project (see @reproducibility). The repository contains all necessary data files and code to replicate our findings.

For brevity this document only focuses on the findings which are included in the report (and/or closely related)

Data used for these analysis can be reproduced by runing the data cleaning file in the repository [link/name]. The data was then split into two datasets, one containing income outliers and one with income outliers removed. The no outliers dataset is used in any analyses which include pay related variables. The outlier exclusion criteria are the same as those in Study 1. In this dataset 10.1% (183) cases are removed in the no outlier dataset.

Data quality check - Income outlier filtering:

Original dataset: 1814 rows

After removing income outliers: 1631 rows

Income outliers removed: 183 ( 10.1 %)

5.1 Pay Comparison

First we explore subjective perceptions of Pay, where participants selected whether they believed they were paid more or less than in-house workers. The analysis is conducted using simple crosstabulations across key demographic variables; Sex, Age, Eethnicity, Region, Income group (e.g. High, Low or Middle), Education Band (High, Low, Middle) and Place of Birth (UK, Not UK)

label	variable	Pros_And_Cons_Pay				Total
		Don't know	I get paid less	I get paid more	Neither / no impact
Sex	Male	68 (7.42%)	170 (18.56%)	291 (31.77%)	387 (42.25%)	916 (56.16%)
	Female	77 (10.81%)	111 (15.59%)	152 (21.35%)	372 (52.25%)	712 (43.65%)
	Other	0 (0%)	0 (0%)	1 (100.00%)	0 (0%)	1 (0.06%)
	Prefer not to say	2 (100.00%)	0 (0%)	0 (0%)	0 (0%)	2 (0.12%)
	Total	147 (9.01%)	281 (17.23%)	444 (27.22%)	759 (46.54%)	1631 (100.00%)
Age	median	39.0	38.0	33.0	40.0	37.0
	mean	39.8	38.5	35.7	40.9	39.0
	std dev	13.2	12.8	11.9	13.5	13.1
Ethnicity	African	13 (7.93%)	27 (16.46%)	52 (31.71%)	72 (43.90%)	164 (10.16%)
	Any other Asian background	0 (0%)	4 (16.67%)	13 (54.17%)	7 (29.17%)	24 (1.49%)
	Any other Black, Black British, or Caribbean background	1 (7.14%)	6 (42.86%)	3 (21.43%)	4 (28.57%)	14 (0.87%)
	Any other ethnic group	1 (33.33%)	0 (0%)	2 (66.67%)	0 (0%)	3 (0.19%)
	Any other Mixed / Multiple ethnic background	1 (14.29%)	2 (28.57%)	0 (0%)	4 (57.14%)	7 (0.43%)
	Any other White background	7 (8.75%)	15 (18.75%)	23 (28.75%)	35 (43.75%)	80 (4.96%)
	Arab	1 (14.29%)	1 (14.29%)	3 (42.86%)	2 (28.57%)	7 (0.43%)
	Bangladeshi	2 (11.76%)	3 (17.65%)	6 (35.29%)	6 (35.29%)	17 (1.05%)
	Caribbean	5 (20.83%)	1 (4.17%)	9 (37.50%)	9 (37.50%)	24 (1.49%)
	Chinese	1 (7.14%)	3 (21.43%)	2 (14.29%)	8 (57.14%)	14 (0.87%)
	Don’t think of myself as any of these	1 (100.00%)	0 (0%)	0 (0%)	0 (0%)	1 (0.06%)
	English / Welsh / Scottish / Northern Irish / British	94 (8.79%)	176 (16.45%)	274 (25.61%)	526 (49.16%)	1070 (66.29%)
	Gypsy or Irish Traveller	0 (0%)	0 (0%)	2 (100.00%)	0 (0%)	2 (0.12%)
	Indian	5 (9.09%)	12 (21.82%)	9 (16.36%)	29 (52.73%)	55 (3.41%)
	Irish	0 (0%)	6 (37.50%)	1 (6.25%)	9 (56.25%)	16 (0.99%)
	Pakistani	5 (13.16%)	7 (18.42%)	14 (36.84%)	12 (31.58%)	38 (2.35%)
	Prefer not to say	2 (40.00%)	1 (20.00%)	0 (0%)	2 (40.00%)	5 (0.31%)
	Roma	0 (0%)	0 (0%)	0 (0%)	2 (100.00%)	2 (0.12%)
	White and Asian	1 (6.67%)	3 (20.00%)	3 (20.00%)	8 (53.33%)	15 (0.93%)
	White and Black African	1 (3.12%)	6 (18.75%)	16 (50.00%)	9 (28.12%)	32 (1.98%)
	White and Black Caribbean	2 (8.33%)	6 (25.00%)	6 (25.00%)	10 (41.67%)	24 (1.49%)
	Total	143 (8.86%)	279 (17.29%)	438 (27.14%)	754 (46.72%)	1614 (100.00%)
Ethnicity_Collapsed	White British	94 (8.79%)	176 (16.45%)	274 (25.61%)	526 (49.16%)	1070 (66.34%)
	Arab	1 (14.29%)	1 (14.29%)	3 (42.86%)	2 (28.57%)	7 (0.43%)
	Asian/Asian British	13 (8.78%)	29 (19.59%)	44 (29.73%)	62 (41.89%)	148 (9.18%)
	Black/African/Caribbean/Black British	19 (9.41%)	34 (16.83%)	64 (31.68%)	85 (42.08%)	202 (12.52%)
	Mixed/Multiple ethnic groups	5 (6.41%)	17 (21.79%)	25 (32.05%)	31 (39.74%)	78 (4.84%)
	Other ethnic group	1 (33.33%)	0 (0%)	2 (66.67%)	0 (0%)	3 (0.19%)
	Prefer not to say	2 (40.00%)	1 (20.00%)	0 (0%)	2 (40.00%)	5 (0.31%)
	White Other	7 (7.00%)	21 (21.00%)	26 (26.00%)	46 (46.00%)	100 (6.20%)
	Total	142 (8.80%)	279 (17.30%)	438 (27.15%)	754 (46.75%)	1613 (100.00%)
Region	London	26 (9.42%)	47 (17.03%)	92 (33.33%)	111 (40.22%)	276 (16.92%)
	East Midlands	10 (7.81%)	18 (14.06%)	33 (25.78%)	67 (52.34%)	128 (7.85%)
	East of England	15 (11.19%)	30 (22.39%)	34 (25.37%)	55 (41.04%)	134 (8.22%)
	North East	5 (7.25%)	13 (18.84%)	19 (27.54%)	32 (46.38%)	69 (4.23%)
	North West	20 (9.43%)	28 (13.21%)	62 (29.25%)	102 (48.11%)	212 (13.00%)
	Northern Ireland	2 (7.14%)	7 (25.00%)	4 (14.29%)	15 (53.57%)	28 (1.72%)
	Scotland	5 (4.81%)	24 (23.08%)	24 (23.08%)	51 (49.04%)	104 (6.38%)
	South East	21 (9.29%)	39 (17.26%)	53 (23.45%)	113 (50.00%)	226 (13.86%)
	South West	14 (11.02%)	24 (18.90%)	32 (25.20%)	57 (44.88%)	127 (7.79%)
	Wales	4 (6.15%)	12 (18.46%)	18 (27.69%)	31 (47.69%)	65 (3.99%)
	West Midlands	14 (9.79%)	25 (17.48%)	48 (33.57%)	56 (39.16%)	143 (8.77%)
	Yorkshire and the Humber	11 (9.24%)	14 (11.76%)	25 (21.01%)	69 (57.98%)	119 (7.30%)
	Total	147 (9.01%)	281 (17.23%)	444 (27.22%)	759 (46.54%)	1631 (100.00%)
income_group	Mid	60 (7.22%)	159 (19.13%)	235 (28.28%)	377 (45.37%)	831 (53.27%)
	High	17 (5.06%)	49 (14.58%)	110 (32.74%)	160 (47.62%)	336 (21.54%)
	Low	49 (12.47%)	67 (17.05%)	89 (22.65%)	188 (47.84%)	393 (25.19%)
	Total	126 (8.08%)	275 (17.63%)	434 (27.82%)	725 (46.47%)	1560 (100.00%)
Education_Band	High	73 (7.53%)	173 (17.84%)	315 (32.47%)	409 (42.16%)	970 (59.47%)
	Low	30 (18.63%)	23 (14.29%)	26 (16.15%)	82 (50.93%)	161 (9.87%)
	Mid	44 (8.80%)	85 (17.00%)	103 (20.60%)	268 (53.60%)	500 (30.66%)
	Total	147 (9.01%)	281 (17.23%)	444 (27.22%)	759 (46.54%)	1631 (100.00%)
BORNUK_binary	Born in UK	100 (8.83%)	182 (16.06%)	283 (24.98%)	568 (50.13%)	1133 (69.47%)
	Not born in UK	36 (7.86%)	97 (21.18%)	153 (33.41%)	172 (37.55%)	458 (28.08%)
	Prefer not to say	11 (27.50%)	2 (5.00%)	8 (20.00%)	19 (47.50%)	40 (2.45%)
	Total	147 (9.01%)	281 (17.23%)	444 (27.22%)	759 (46.54%)	1631 (100.00%)

The pay comparison analysis reveals several notable demographic patterns in perceived pay inequality among outsourced workers. Sex differences are evident, with men more likely to report being paid more than in-house workers (31.77%) compared to women (21.35%), while women more frequently report no pay difference (52.25% vs 42.25%). Age patterns show that those reporting higher pay tend to be younger (mean age 35.7 years) compared to those reporting lower pay (mean age 38.5 years). Education disparities are striking, with highly educated workers much more likely to report being paid more (32.47%) compared to those with low education (16.15%). Place of birth differences emerge, with workers not born in the UK more likely to report being paid more (33.41%) than UK-born workers (24.98%), though they also report higher rates of being paid less (21.18% vs 16.06%). Income group patterns show that high-income workers are most likely to report being paid more as expected (32.74%), while low-income workers show the highest rates of uncertainty about their pay situation (12.47% “don’t know”).

5.2 Work Preferences

Next we explore simple counts/percentages of Outsourced workers preferences for in-house vs outsourced work.

Work Preference	Count	Percentage
I have no preference	718	39.6
I would prefer to be an in-house worker	287	15.8
I would prefer to be an outsourced worker	199	11.0
I would strongly prefer to be an in-house worker	295	16.3
I would strongly prefer to be an outsourced worker	126	6.9
Not sure	189	10.4

The work preferences data show that the largest group of outsourced workers (39.6%) express no preference between in-house and outsourced employment. However, among those with preferences, there is a clear preference for in-house work, with 32.1% preferring in-house positions (16.3% strongly + 15.8% prefer) compared to 17.9% preferring outsourced work (6.9% strongly + 11.0% prefer).

5.3 Job Motivation

Here we examine the motivational factors that influence outsourced workers’ decisions to remain in their current roles. Using a descriptive approach, we present the frequency and percentage distribution of responses across eleven distinct motivational categories, ranging from intrinsic factors (job satisfaction, workplace culture) to extrinsic factors (pay, location convenience) and personal circumstances (health conditions, caregiving responsibilities).

Reason for Current Role	Count	Percentage
My job is in a convenient location	748	41.2
The pay is good	739	40.7
I like doing this kind of work	728	40.1
I like my colleagues	652	35.9
I can work flexibly in a way which suits me	581	32.0
I like the workplace culture	502	27.7
This was the best job available to me	437	24.1
It is helping me develop skills and experience I need to progress	428	23.6
I can do the job alongside childcare or caring for others	264	14.6
I can do the job alongside managing my health conditions	235	13.0
I do not have the formal qualifications I need to do another job I would prefer	163	9.0

The job motivation analysis shows the key factors driving outsourced workers’ employment decisions. Practical considerations dominate, with job location convenience being the most frequently cited reason (41.2%), followed closely by pay satisfaction (40.7%) and intrinsic job satisfaction (40.1%). Social factors also play a significant role, with over one-third (35.9%) citing positive relationships with colleagues. Flexibility, often assumed to be a primary motivation for outsourced work, ranks fifth at 32.0%. Workplace culture is important to just over a quarter (27.7%) of workers. Necessity-driven motivations are less common but notable, with 24.1% stating this was the best job available and 23.6% viewing it as a stepping stone for skill development. Personal circumstances account for smaller proportions: 14.6% balance the job with caregiving responsibilities, 13.0% manage health conditions, and only 9.0% remain due to qualification constraints (although it is possible that social desirability is playing a role in participants willingness to respond to questions about their level of qualifications and its relation to their employment possibilities).

5.4 Pros and Cons of Outsourced Work

Here we explore outsourced workers’ comparative assessments of their employment conditions relative to hypothetical in-house positions. Participants rated fourteen distinct workplace dimensions on a scale ranging from “less/worse” to “more/better” compared to in-house workers.

The pros and cons suggests areas of disadvantage for outsourced workers compared to hypothetical in-house positions. Career development emerges as the most problematic area, with 24% reporting worse opportunities for progression/promotion and 20% citing reduced access to training and development. Job security concerns are similarly prominent, with 20% reporting worse access to job security. Workplace relationships and engagement show notable deficits, with 19% feeling less connected to colleagues and 17% feeling less invested in their role. Workers’ rights and voice present challenges, with 19% finding it harder to assert rights at work.

Flexibility stands out as a key advantage, with 41.8% reporting better flexibility compared to only 14.2% reporting worse flexibility - making it the strongest positive aspect of outsourced work. Pay perceptions are mixed, with 17% reporting worse pay, though this varies significantly by demographic group as shown in the earlier analysis.

The overall pattern shows that while outsourced workers may benefit from increased flexibility, they face systematic disadvantages in career development, job security, workplace relationships, and employee voice. Most concerning is that career progression and training opportunities - crucial for long-term economic mobility - rank as the most problematic areas for outsourced workers.

5.5 Cumulative Burden

Here we quantify the cumulative burden of negative work experiences among outsourced workers by transforming the categorical responses from the pros and cons analysis into numerical scores (-1 for negative, 0 for neutral, +1 for positive). We calculate the total number of negative outcomes per respondent across all 14 workplace dimensions and examine the distribution of these counts within the sample. The analysis includes a focused examination of workers who report being paid less than in-house colleagues, investigating whether pay disadvantage is associated with broader patterns of workplace disadvantage.

Number of Negative Outcomes	Count	Percentage
No negative impacts	699	38.5
1-2 negative outcomes	393	21.7
3-4 negative outcomes	294	16.2
5+ negative outcomes	428	23.6

The negative Impacts analysis reveals the cumulative burden of workplace disadvantages among outsourced workers. The distribution shows significant polarisation: 38.5% of workers report no negative outcomes, while 23.6% experience five or more negative outcomes across the fourteen workplace dimensions (mean = 2.51, median = 1).

Most striking is the relationship between pay disadvantage and cumulative negative outcomes. Among workers who report being paid less than in-house colleagues, 96.4% experience at least one additional negative outcome beyond pay - only 3.6% report pay disadvantage as an isolated issue. The concentration of negative outcomes among this group is severe: 69.7% experience five or more total negative outcomes (including pay) compared to just 23.6% in the overall population - nearly a threefold difference.

The pattern suggests that pay disadvantage rarely occurs in isolation but is typically accompanied by broader workplace disadvantages. Workers reporting pay disadvantage show dramatically higher rates of multiple negative outcomes (10.1% have 4+ negatives including pay vs 7.4% overall), indicating that pay inequity is a marker of comprehensive workplace disadvantage rather than an isolated issue.

5.6 Statistical Analysis

Continuing the analysis of pros and cons of outsourced working this section presents descriptive analysis of mean negative outcomes across demographic groups, followed by regression modeling to identify predictors. The analysis compares Poisson, linear, and negative binomial regression models using fit statistics (AIC, deviance, RMSE) to select the best-fitting model. Results are presented through a model comparison table, coefficients table with rate ratios and confidence intervals, forest plot visualisation of rate ratios, and predicted values for different income groups. The analysis identifies which demographic characteristics are associated with higher rates of negative workplace outcomes among outsourced workers.

The statistical analysis reveals significant demographic predictors of negative workplace outcomes among outsourced workers. Model selection favoured negative binomial regression due to overdispersion (ratio = 3.387), with this model showing the best fit (AIC = 6519.5) compared to Poisson (AIC = 8173.4) and linear models (AIC = 7700.3).

Overdispersion ratio: 3.387 

If > 1.5, consider negative binomial or quasi-Poisson

Negative binomial model fitted due to overdispersion

Model Type	AIC	Deviance	R-Squared	RMSE
Full Poisson	8,173.415	5,128.115		1.822
Full Linear	7,700.326		0.07	2.871
Full Negative Binomial	6,519.478	1,690.507		1.046

Variable	Coefficient	Std Error	Rate Ratio	95% CI Lower	95% CI Upper	P-Value	Significant
income_groupHigh	-0.2529	0.0870	0.7765	-0.4233	-0.0808	0.0037	TRUE
income_groupLow	0.1055	0.0823	1.1112	-0.0578	0.2706	0.2002	FALSE
Ethnicity_CollapsedArab	-0.3962	0.5069	0.6729	-1.3302	0.6882	0.4345	FALSE
Ethnicity_CollapsedAsian/Asian British	0.0185	0.1248	1.0186	-0.2230	0.2658	0.8823	FALSE
Ethnicity_CollapsedBlack/African/Caribbean/Black British	-0.2726	0.1196	0.7614	-0.5034	-0.0391	0.0226	TRUE
Ethnicity_CollapsedMixed/Multiple ethnic groups	-0.0598	0.1577	0.9419	-0.3632	0.2566	0.7045	FALSE
Ethnicity_CollapsedOther ethnic group	-0.6512	0.7920	0.5214	-2.0656	1.1238	0.4109	FALSE
Ethnicity_CollapsedPrefer not to say	0.4314	0.6492	1.5395	-0.7254	1.8909	0.5063	FALSE
Ethnicity_CollapsedWhite Other	-0.1453	0.1543	0.8648	-0.4388	0.1567	0.3462	FALSE
SexFemale	-0.2371	0.0688	0.7889	-0.3719	-0.1021	0.0006	TRUE
SexOther	-1.2128	1.5148	0.2973	-4.5284	2.5177	0.4233	FALSE
Education_BandLow	-0.4324	0.1255	0.6490	-0.6781	-0.1815	0.0006	TRUE
Education_BandMid	-0.3036	0.0783	0.7382	-0.4595	-0.1469	0.0001	TRUE
BORNUK_binaryNot born in UK	0.3230	0.0916	1.3813	0.1483	0.4994	0.0004	TRUE
BORNUK_binaryPrefer not to say	-0.8291	0.2663	0.4364	-1.3420	-0.2989	0.0018	TRUE
Age	-0.0058	0.0027	0.9942	-0.0112	-0.0004	0.0339	TRUE
RegionEast Midlands	-0.0311	0.1445	0.9694	-0.3125	0.2551	0.8298	FALSE
RegionEast of England	0.1038	0.1425	1.1094	-0.1761	0.3884	0.4664	FALSE
RegionNorth East	-0.0869	0.1838	0.9168	-0.4451	0.2834	0.6365	FALSE
RegionNorth West	-0.0799	0.1253	0.9232	-0.3260	0.1673	0.5237	FALSE
RegionNorthern Ireland	0.1957	0.2571	1.2161	-0.2924	0.7212	0.4466	FALSE
RegionScotland	0.0312	0.1563	1.0317	-0.2739	0.3430	0.8418	FALSE
RegionSouth East	0.1095	0.1232	1.1157	-0.1339	0.3539	0.3740	FALSE
RegionSouth West	0.0866	0.1461	1.0904	-0.1997	0.3776	0.5534	FALSE
RegionWales	0.0235	0.1844	1.0238	-0.3374	0.3965	0.8985	FALSE
RegionWest Midlands	-0.0525	0.1400	0.9489	-0.3246	0.2236	0.7076	FALSE
RegionYorkshire and the Humber	-0.2809	0.1539	0.7551	-0.5837	0.0265	0.0679	FALSE
OutsourcedNonOLgroup 2 agency and long term	-0.0921	0.0972	0.9120	-0.2819	0.1013	0.3434	FALSE
OutsourcedNonOLgroup 3 5 or 6 indicators and long term	0.1262	0.1172	1.1345	-0.1010	0.3611	0.2816	FALSE

Education emerges as the strongest predictor, with both mid-education (rate ratio = 0.738) and low-education workers (rate ratio = 0.649) experiencing significantly fewer negative outcomes than highly educated workers. This counterintuitive finding suggests that higher education may increase expectations or awareness of workplace disadvantages rather than protecting against them.

Place of birth shows significant effects, with workers not born in the UK experiencing 38% more negative outcomes (rate ratio = 1.38) compared to UK-born workers, while those preferring not to disclose birth status report 56% fewer negative outcomes (rate ratio = 0.436). Descriptive patterns show that workers not born in the UK have the highest mean negative outcomes (3.2), while those born in the UK average 2.33 negative outcomes.

Ethnicity shows some variation, with Black/African/Caribbean workers reporting 24% fewer negative outcomes than White British workers (rate ratio = 0.761).

Gender differences are evident, with female workers experiencing 21% fewer negative outcomes than male workers (rate ratio = 0.789). Age shows a protective effect, with each additional year associated with slightly fewer negative outcomes (rate ratio = 0.994).

Income effects are notable, with high-income workers experiencing 22% fewer negative outcomes than mid-income workers (rate ratio = 0.777). The predicted values plot demonstrates this income effect more clearly by holding all other demographic variables constant. Under these controlled conditions, the model predicts that high-income workers will experience approximately 2.14 negative outcomes compared to 2.75 for mid-income workers and 3.56 for low-income workers - a reduction of 0.61 and 1.42 (respectively) negative outcomes purely attributable to income level.

5.7 Work Conditions

Next we examine specific employment conditions that characterise outsourced work arrangements, focusing on five key dimensions: guaranteed hours, notice periods for working schedules, advance warning of shift cancellations, compensation for cancelled shifts, and sick pay provision. The analysis presents descriptive statistics for each condition and conducts cross-tabulations with demographic variables to identify potential disparities in work conditions across different groups. Statistical tests (chi-square or Fisher’s exact tests) are employed to assess the significance of observed associations, with effect sizes calculated using Cramér’s V.

 Guaranteed_Hours :

                       1-8 hours                      16-24 hours 
                             129                              183 
                     25-35 hours                        35+ hours 
                             300                              972 
                      9-15 hours No guaranteed hours (zero hours) 
                             121                              109 
Total responses: 1814 

 Notice_Of_Working_Hours :

                                                                    1-2 weeks 
                                                                          238 
                                                                     1-3 days 
                                                                          282 
                                                                     4-6 days 
                                                                          318 
                                                              4 weeks or more 
                                                                          183 
                                                           Less than 24 hours 
                                                                          132 
                                      More than 2 weeks but less than 4 weeks 
                                                                           99 
Not applicable – my job does not involve variable working hours or shift work 
                                                                          562 
Total responses: 1814 

 Notice_Of_Cancelled_Shifts :

                                                                           No 
                                                                         1054 
Not applicable - my job does not involve variable working hours or shift work 
                                                                          353 
                                                                          Yes 
                                                                          407 
Total responses: 1814 

 Cancelled_Shift_Pay :

    0%  1-24%   100% 25-49% 50-74% 75-99%   <NA> 
    92    104     44     80     50     37   1407 
Total responses: 1814 

 Sick_Pay :

                                                                                            I don’t know / I’m not sure 
                                                                                                                    114 
                                                             No, I do not have access to sick pay if I am off work sick 
                                                                                                                    263 
Yes, my employer pays me sick pay which is above the basic rate of statutory sick pay (£116.75 a week) but below my usu 
                                                                                                                    268 
                       Yes, my employer pays my usual rate of pay for my usual working hours, so I receive my usual pay 
                                                                                                                    918 
                                            Yes, my employer pays the basic rate of statutory sick pay (£116.75 a week) 
                                                                                                                    251 
Total responses: 1814 

Work Condition Variable	Total Responses	Missing Values	Most Common Response	Percentage
Guaranteed Hours	1,814	0	35+ hours	53.6%
Notice of Working Hours	1,814	0	Not applicable – my job does not involve variable working hours or shift work	31%
Notice of Cancelled Shifts	1,814	0	No	58.1%
Cancelled Shift Pay	407	1,407	1-24%	25.6%
Sick Pay	1,814	0	Yes, my employer pays my usual rate of pay for my usual working hours, so I receive my usual pay	50.6%

label	variable	income_group				Total
		Mid	High	Low	NA
Notice_Of_Working_Hours_Simplified	1-2 weeks	111 (13.36%)	52 (15.48%)	46 (11.70%)	5	214 (13.12%)
	4 weeks or more	88 (10.59%)	41 (12.20%)	21 (5.34%)	12	162 (9.93%)
	Less than a week	316 (38.03%)	108 (32.14%)	209 (53.18%)	23	656 (40.22%)
	More than 2 weeks but less than 4 weeks	57 (6.86%)	14 (4.17%)	18 (4.58%)	2	91 (5.58%)
	Not applicable – my job does not involve variable working hours or shift work	259 (31.17%)	121 (36.01%)	99 (25.19%)	29	508 (31.15%)
	Total	831 (53.27%)	336 (21.54%)	393 (25.19%)	71	1631 (100.00%)

label	variable	income_group				Total
		Mid	High	Low	NA
Guaranteed_Hours	1-8 hours	48 (5.78%)	12 (3.57%)	41 (10.43%)	7	108 (6.62%)
	16-24 hours	48 (5.78%)	14 (4.17%)	103 (26.21%)	13	178 (10.91%)
	25-35 hours	116 (13.96%)	52 (15.48%)	84 (21.37%)	8	260 (15.94%)
	35+ hours	542 (65.22%)	231 (68.75%)	62 (15.78%)	33	868 (53.22%)
	9-15 hours	41 (4.93%)	14 (4.17%)	56 (14.25%)	3	114 (6.99%)
	No guaranteed hours (zero hours)	36 (4.33%)	13 (3.87%)	47 (11.96%)	7	103 (6.32%)
	Total	831 (53.27%)	336 (21.54%)	393 (25.19%)	71	1631 (100.00%)

label	variable	income_group				Total
		Mid	High	Low	NA
Sick_Pay	I don’t know / I’m not sure	41 (4.93%)	12 (3.57%)	39 (9.92%)	18	110 (6.74%)
	No, I do not have access to sick pay if I am off work sick	117 (14.08%)	23 (6.85%)	98 (24.94%)	15	253 (15.51%)
	Yes, my employer pays me sick pay which is above the basic rate of statutory sick pay (£116.75 a week) but below my usu	125 (15.04%)	56 (16.67%)	50 (12.72%)	5	236 (14.47%)
	Yes, my employer pays my usual rate of pay for my usual working hours, so I receive my usual pay	423 (50.90%)	203 (60.42%)	155 (39.44%)	26	807 (49.48%)
	Yes, my employer pays the basic rate of statutory sick pay (£116.75 a week)	125 (15.04%)	42 (12.50%)	51 (12.98%)	7	225 (13.80%)
	Total	831 (53.27%)	336 (21.54%)	393 (25.19%)	71	1631 (100.00%)

label	variable	Ethnicity_Collapsed									Total
		White British	Arab	Asian/Asian British	Black/African/Caribbean/Black British	Mixed/Multiple ethnic groups	Other ethnic group	Prefer not to say	White Other	NA
Notice_Of_Working_Hours_Simplified	1-2 weeks	152 (12.93%)	1 (11.11%)	25 (14.79%)	36 (15.32%)	11 (12.09%)	0 (0%)	0 (0%)	11 (10.38%)	2	238 (13.12%)
	4 weeks or more	135 (11.48%)	1 (11.11%)	17 (10.06%)	16 (6.81%)	5 (5.49%)	0 (0%)	0 (0%)	7 (6.60%)	2	183 (10.09%)
	Less than a week	419 (35.63%)	5 (55.56%)	76 (44.97%)	130 (55.32%)	42 (46.15%)	2 (66.67%)	1 (20.00%)	48 (45.28%)	9	732 (40.35%)
	More than 2 weeks but less than 4 weeks	66 (5.61%)	0 (0%)	7 (4.14%)	9 (3.83%)	7 (7.69%)	1 (33.33%)	0 (0%)	7 (6.60%)	2	99 (5.46%)
	Not applicable – my job does not involve variable working hours or shift work	404 (34.35%)	2 (22.22%)	44 (26.04%)	44 (18.72%)	26 (28.57%)	0 (0%)	4 (80.00%)	33 (31.13%)	5	562 (30.98%)
	Total	1176 (65.55%)	9 (0.50%)	169 (9.42%)	235 (13.10%)	91 (5.07%)	3 (0.17%)	5 (0.28%)	106 (5.91%)	20	1814 (100.00%)

Chi-square assumption check - Minimum expected frequency: 0.16

 ✗ Chi-square assumptions violated - using Fisher's exact test

                 Test p.value Cramers.V Effect.Size Significance
1 Fisher's exact test < 0.001     0.095  Negligible  Significant

Post-hoc Analysis Assessment:

✓ Overall association is significant
✓ Multiple categories present - post-hoc pairwise comparisons recommended

Pairwise Fisher's Exact Tests (Bonferroni corrected):
                                                              Comparison
1                          White British vs Mixed/Multiple ethnic groups
2                 White British vs Black/African/Caribbean/Black British
3                                           White British vs White Other
4                                   White British vs Asian/Asian British
5                                                  White British vs Arab
6                                    White British vs Other ethnic group
7                                     White British vs Prefer not to say
8  Mixed/Multiple ethnic groups vs Black/African/Caribbean/Black British
9                            Mixed/Multiple ethnic groups vs White Other
10                   Mixed/Multiple ethnic groups vs Asian/Asian British
11                                  Mixed/Multiple ethnic groups vs Arab
12                    Mixed/Multiple ethnic groups vs Other ethnic group
13                     Mixed/Multiple ethnic groups vs Prefer not to say
14                  Black/African/Caribbean/Black British vs White Other
15          Black/African/Caribbean/Black British vs Asian/Asian British
16                         Black/African/Caribbean/Black British vs Arab
17           Black/African/Caribbean/Black British vs Other ethnic group
18            Black/African/Caribbean/Black British vs Prefer not to say
19                                    White Other vs Asian/Asian British
20                                                   White Other vs Arab
21                                     White Other vs Other ethnic group
22                                      White Other vs Prefer not to say
23                                           Asian/Asian British vs Arab
24                             Asian/Asian British vs Other ethnic group
25                              Asian/Asian British vs Prefer not to say
26                                            Arab vs Other ethnic group
27                                             Arab vs Prefer not to say
28                               Other ethnic group vs Prefer not to say
   p_value p_adjusted significant_adjusted cramers_v effect_size
1   0.1372     1.0000                FALSE     0.073  Negligible
2   0.0002     0.0056                 TRUE     0.170       Small
3   0.2501     1.0000                FALSE     0.066  Negligible
4   0.1092     1.0000                FALSE     0.076  Negligible
5   0.8738     1.0000                FALSE     0.040  Negligible
6   0.1946     1.0000                FALSE     0.075  Negligible
7   0.5051     1.0000                FALSE     0.064  Negligible
8   0.1552     1.0000                FALSE     0.143       Small
9   0.9804     1.0000                FALSE     0.046  Negligible
10  0.5085     1.0000                FALSE     0.113       Small
11  0.8414     1.0000                FALSE     0.118       Small
12  0.3663     1.0000                FALSE     0.203       Small
13  0.3311     1.0000                FALSE     0.250       Small
14  0.0624     1.0000                FALSE     0.161       Small
15  0.2136     1.0000                FALSE     0.120       Small
16  0.8594     1.0000                FALSE     0.055  Negligible
17  0.2679     1.0000                FALSE     0.177       Small
18  0.0570     1.0000                FALSE     0.220       Small
19  0.5303     1.0000                FALSE     0.109       Small
20  0.9182     1.0000                FALSE     0.104       Small
21  0.3059     1.0000                FALSE     0.205       Small
22  0.4013     1.0000                FALSE     0.218       Small
23  1.0000     1.0000                FALSE     0.064  Negligible
24  0.2659     1.0000                FALSE     0.205       Small
25  0.2258     1.0000                FALSE     0.204       Small
26  0.6615     1.0000                FALSE     0.604       Large
27  0.2340     1.0000                FALSE     0.571       Large
28  0.0688     1.0000                FALSE     0.803       Large
                                        less_than_week_direction
1           Mixed/Multiple ethnic groups higher (46.2% vs 35.6%)
2  Black/African/Caribbean/Black British higher (55.3% vs 35.6%)
3                            White Other higher (45.3% vs 35.6%)
4                      Asian/Asian British higher (45% vs 35.6%)
5                                   Arab higher (55.6% vs 35.6%)
6                     Other ethnic group higher (66.7% vs 35.6%)
7                            White British higher (35.6% vs 20%)
8  Black/African/Caribbean/Black British higher (55.3% vs 46.2%)
9           Mixed/Multiple ethnic groups higher (46.2% vs 45.3%)
10            Mixed/Multiple ethnic groups higher (46.2% vs 45%)
11                                  Arab higher (55.6% vs 46.2%)
12                    Other ethnic group higher (66.7% vs 46.2%)
13            Mixed/Multiple ethnic groups higher (46.2% vs 20%)
14 Black/African/Caribbean/Black British higher (55.3% vs 45.3%)
15   Black/African/Caribbean/Black British higher (55.3% vs 45%)
16                                  Arab higher (55.6% vs 55.3%)
17                    Other ethnic group higher (66.7% vs 55.3%)
18   Black/African/Caribbean/Black British higher (55.3% vs 20%)
19                             White Other higher (45.3% vs 45%)
20                                  Arab higher (55.6% vs 45.3%)
21                    Other ethnic group higher (66.7% vs 45.3%)
22                             White Other higher (45.3% vs 20%)
23                                    Arab higher (55.6% vs 45%)
24                      Other ethnic group higher (66.7% vs 45%)
25                       Asian/Asian British higher (45% vs 20%)
26                    Other ethnic group higher (66.7% vs 55.6%)
27                                    Arab higher (55.6% vs 20%)
28                      Other ethnic group higher (66.7% vs 20%)

Significant pairwise differences (after Bonferroni correction):
• White British vs Black/African/Caribbean/Black British (p = 0.0056 , Cramér's V = 0.17 )
  Direction: Black/African/Caribbean/Black British higher (55.3% vs 35.6%) 

label	variable	Ethnicity_Collapsed									Total
		White British	Arab	Asian/Asian British	Black/African/Caribbean/Black British	Mixed/Multiple ethnic groups	Other ethnic group	Prefer not to say	White Other	NA
Guaranteed_Hours	1-8 hours	67 (5.70%)	2 (22.22%)	22 (13.02%)	13 (5.53%)	11 (12.09%)	1 (33.33%)	0 (0%)	9 (8.49%)	4	129 (7.11%)
	16-24 hours	124 (10.54%)	1 (11.11%)	15 (8.88%)	22 (9.36%)	7 (7.69%)	0 (0%)	1 (20.00%)	12 (11.32%)	1	183 (10.09%)
	25-35 hours	183 (15.56%)	2 (22.22%)	21 (12.43%)	60 (25.53%)	13 (14.29%)	1 (33.33%)	1 (20.00%)	17 (16.04%)	2	300 (16.54%)
	35+ hours	651 (55.36%)	3 (33.33%)	90 (53.25%)	109 (46.38%)	52 (57.14%)	1 (33.33%)	3 (60.00%)	52 (49.06%)	11	972 (53.58%)
	9-15 hours	78 (6.63%)	0 (0%)	9 (5.33%)	23 (9.79%)	5 (5.49%)	0 (0%)	0 (0%)	4 (3.77%)	2	121 (6.67%)
	No guaranteed hours (zero hours)	73 (6.21%)	1 (11.11%)	12 (7.10%)	8 (3.40%)	3 (3.30%)	0 (0%)	0 (0%)	12 (11.32%)	0	109 (6.01%)
	Total	1176 (65.55%)	9 (0.50%)	169 (9.42%)	235 (13.10%)	91 (5.07%)	3 (0.17%)	5 (0.28%)	106 (5.91%)	20	1814 (100.00%)

Chi-square assumption check - Minimum expected frequency: 0.18

 ✗ Chi-square assumptions violated - using Fisher's exact test

                                  
                                   White British Arab Asian/Asian British
  1-8 hours                                   67    2                  22
  16-24 hours                                124    1                  15
  25-35 hours                                183    2                  21
  35+ hours                                  651    3                  90
  9-15 hours                                  78    0                   9
  No guaranteed hours (zero hours)            73    1                  12
                                  
                                   Black/African/Caribbean/Black British
  1-8 hours                                                           13
  16-24 hours                                                         22
  25-35 hours                                                         60
  35+ hours                                                          109
  9-15 hours                                                          23
  No guaranteed hours (zero hours)                                     8
                                  
                                   Mixed/Multiple ethnic groups
  1-8 hours                                                  11
  16-24 hours                                                 7
  25-35 hours                                                13
  35+ hours                                                  52
  9-15 hours                                                  5
  No guaranteed hours (zero hours)                            3
                                  
                                   Other ethnic group Prefer not to say
  1-8 hours                                         1                 0
  16-24 hours                                       0                 1
  25-35 hours                                       1                 1
  35+ hours                                         1                 3
  9-15 hours                                        0                 0
  No guaranteed hours (zero hours)                  0                 0
                                  
                                   White Other
  1-8 hours                                  9
  16-24 hours                               12
  25-35 hours                               17
  35+ hours                                 52
  9-15 hours                                 4
  No guaranteed hours (zero hours)          12

Fisher's Exact Test Results:

                 Test p.value Cramers.V Effect.Size Significance
1 Fisher's exact test  0.0022     0.082  Negligible  Significant

Post-hoc Analysis Assessment:

✓ Overall association is significant
✓ Multiple categories present - post-hoc pairwise comparisons recommended

Pairwise Fisher's Exact Tests (Bonferroni corrected):
                                                              Comparison
1                          White British vs Mixed/Multiple ethnic groups
2                 White British vs Black/African/Caribbean/Black British
3                                           White British vs White Other
4                                   White British vs Asian/Asian British
5                                                  White British vs Arab
6                                    White British vs Other ethnic group
7                                     White British vs Prefer not to say
8  Mixed/Multiple ethnic groups vs Black/African/Caribbean/Black British
9                            Mixed/Multiple ethnic groups vs White Other
10                   Mixed/Multiple ethnic groups vs Asian/Asian British
11                                  Mixed/Multiple ethnic groups vs Arab
12                    Mixed/Multiple ethnic groups vs Other ethnic group
13                     Mixed/Multiple ethnic groups vs Prefer not to say
14                  Black/African/Caribbean/Black British vs White Other
15          Black/African/Caribbean/Black British vs Asian/Asian British
16                         Black/African/Caribbean/Black British vs Arab
17           Black/African/Caribbean/Black British vs Other ethnic group
18            Black/African/Caribbean/Black British vs Prefer not to say
19                                    White Other vs Asian/Asian British
20                                                   White Other vs Arab
21                                     White Other vs Other ethnic group
22                                      White Other vs Prefer not to say
23                                           Asian/Asian British vs Arab
24                             Asian/Asian British vs Other ethnic group
25                              Asian/Asian British vs Prefer not to say
26                                            Arab vs Other ethnic group
27                                             Arab vs Prefer not to say
28                               Other ethnic group vs Prefer not to say
   p_value p_adjusted significant_adjusted cramers_v effect_size
1   0.2446     1.0000                FALSE     0.078  Negligible
2   0.0024     0.0672                FALSE     0.119       Small
3   0.2198     1.0000                FALSE     0.075  Negligible
4   0.0348     0.9742                FALSE     0.102       Small
5   0.1588     1.0000                FALSE     0.072  Negligible
6   0.2697     1.0000                FALSE     0.068  Negligible
7   0.9340     1.0000                FALSE     0.035  Negligible
8   0.0520     1.0000                FALSE     0.181       Small
9   0.2589     1.0000                FALSE     0.181       Small
10  0.8752     1.0000                FALSE     0.086  Negligible
11  0.3157     1.0000                FALSE     0.192       Small
12  0.4755     1.0000                FALSE     0.164       Small
13  0.7427     1.0000                FALSE     0.145       Small
14  0.0082     0.2296                FALSE     0.213       Small
15  0.0014     0.0392                 TRUE     0.228       Small
16  0.1866     1.0000                FALSE     0.164       Small
17  0.3967     1.0000                FALSE     0.141       Small
18  0.8804     1.0000                FALSE     0.086  Negligible
19  0.5505     1.0000                FALSE     0.122       Small
20  0.6467     1.0000                FALSE     0.150       Small
21  0.4343     1.0000                FALSE     0.177       Small
22  1.0000     1.0000                FALSE     0.122       Small
23  0.5055     1.0000                FALSE     0.119       Small
24  0.5111     1.0000                FALSE     0.127       Small
25  0.7600     1.0000                FALSE     0.112       Small
26  1.0000     1.0000                FALSE     0.272       Small
27  0.9096     1.0000                FALSE     0.413      Medium
28  0.8012     1.0000                FALSE     0.577       Large
                                                    hours_35plus_direction
1           Mixed/Multiple ethnic groups higher 35+ hours (57.1% vs 55.4%)
2                          White British higher 35+ hours (55.4% vs 46.4%)
3                          White British higher 35+ hours (55.4% vs 49.1%)
4                          White British higher 35+ hours (55.4% vs 53.3%)
5                          White British higher 35+ hours (55.4% vs 33.3%)
6                          White British higher 35+ hours (55.4% vs 33.3%)
7                        Prefer not to say higher 35+ hours (60% vs 55.4%)
8           Mixed/Multiple ethnic groups higher 35+ hours (57.1% vs 46.4%)
9           Mixed/Multiple ethnic groups higher 35+ hours (57.1% vs 49.1%)
10          Mixed/Multiple ethnic groups higher 35+ hours (57.1% vs 53.3%)
11          Mixed/Multiple ethnic groups higher 35+ hours (57.1% vs 33.3%)
12          Mixed/Multiple ethnic groups higher 35+ hours (57.1% vs 33.3%)
13                       Prefer not to say higher 35+ hours (60% vs 57.1%)
14                           White Other higher 35+ hours (49.1% vs 46.4%)
15                   Asian/Asian British higher 35+ hours (53.3% vs 46.4%)
16 Black/African/Caribbean/Black British higher 35+ hours (46.4% vs 33.3%)
17 Black/African/Caribbean/Black British higher 35+ hours (46.4% vs 33.3%)
18                       Prefer not to say higher 35+ hours (60% vs 46.4%)
19                   Asian/Asian British higher 35+ hours (53.3% vs 49.1%)
20                           White Other higher 35+ hours (49.1% vs 33.3%)
21                           White Other higher 35+ hours (49.1% vs 33.3%)
22                       Prefer not to say higher 35+ hours (60% vs 49.1%)
23                   Asian/Asian British higher 35+ hours (53.3% vs 33.3%)
24                   Asian/Asian British higher 35+ hours (53.3% vs 33.3%)
25                       Prefer not to say higher 35+ hours (60% vs 53.3%)
26                                      Similar 35+ hours (33.3% vs 33.3%)
27                       Prefer not to say higher 35+ hours (60% vs 33.3%)
28                       Prefer not to say higher 35+ hours (60% vs 33.3%)

Significant pairwise differences (after Bonferroni correction):
• Black/African/Caribbean/Black British vs Asian/Asian British (p = 0.0392 , Cramér's V = 0.228 )
  Direction: Asian/Asian British higher 35+ hours (53.3% vs 46.4%) 

The work conditions analysis reveals varying degrees of job security and workplace protections among outsourced workers. Hour guarantees show moderate security, with 53.6% having 35+ hour contracts, though 6.0% work zero-hours contracts and 23.9% have part-time guarantees (1-24 hours). Income disparities are stark in hour guarantees: low-income workers face zero-hours contracts at triple the rate (12.0%) compared to high-income (3.7%) and mid-income workers (4.3%), while only 15.8% of low-income workers have 35+ hour guarantees versus 64.5% of high-income workers.

Scheduling predictability presents challenges, with 40.3% of workers receiving less than one week’s notice of working hours, while 28.7% receive one week or more notice. Shift cancellations affect a significant minority, with 22.4% experiencing cancelled shifts. Compensation for cancelled shifts shows concerning patterns: among those experiencing cancellations, 22.6% receive no pay, 45.3% receive partial compensation (1-49%), and only 32.2% receive most or full pay (50-100%).

Sick pay provision is mixed, with half (50.6%) receiving full usual pay when sick, but 14.5% having no sick pay access and 13.8% limited to statutory minimum (£116.75/week). Uncertainty about entitlements affects 6.3% who don’t know their sick pay rights.

The overall pattern suggests a two-tier system where higher-income outsourced workers enjoy more stable conditions (guaranteed hours, better scheduling predictability) while lower-income workers face greater insecurity through zero-hours contracts and unpredictable scheduling. This stratification within outsourced work creates differential vulnerabilities across income groups.

5.8 Rights Violations

This analysis examines reported workplace rights violations among outsourced workers, focusing on the prevalence and nature of violations across different categories. The analysis employs descriptive statistics to document the frequency of various rights violations and explores demographic correlates of violation experiences. Cross-tabulations with key variables (income group, ethnicity, employment type) are conducted to identify vulnerable populations, with statistical significance assessed through appropriate tests and effect sizes calculated where applicable.

Rights Violation	Count	Percentage (%)
Not being paid on time	199	11
Not being given time off that I am entitled to	203	11
Not being given pay that I am entitled to while being off sick	197	11
Not having adequate health and safety protections	178	10
Not being paid the full amount I am entitled to for the work I have completed	167	9
Not being paid for paid leave that I am entitled to	158	9
Not being provided with a pay slip	164	9

The rights violations analysis reveals that nearly half of outsourced workers experience at least one workplace rights violation, with 56.3% reporting no violations. Leave entitlement issues are the most prevalent violation (11.2%), followed closely by payment timing problems (11.0%) and sick pay violations (10.9%). Health and safety protections are inadequate for 9.8% of workers, while pay slip provision (9.0%) and correct payment amounts (9.2%) also represent concerns.

5.9 Discrimination Analysis

This analysis examines reported experiences of discrimination among outsourced workers across multiple dimensions including age, disability, ethnicity, nationality, religion, and sex. The analysis employs descriptive statistics to document the prevalence of different types of discrimination and conducts cross-tabulations with demographic variables to identify patterns and vulnerable populations. Statistical tests assess the significance of observed associations, with particular attention to intersectional effects where multiple forms of discrimination may compound disadvantage among specific groups.

5.9.1 Sex-based Discrimination

===  SEX-BASED  DISCRIMINATION ANALYSIS ===
Total valid responses for Inhouse_Discrimination_Sex : 1727 

The sex-based discrimination analysis reveals significant variation in discrimination experiences across demographic groups, with Black workers reporting the highest rates of sex-based discrimination (42.9% experienced discrimination), followed by workers born outside the UK (41.9%). Asian workers also face elevated rates (38.2%), while female workers report discrimination at 31.3%. The overall rate among all outsourced workers is 30.8%. Notably, Black workers show the lowest percentage reporting “Never” experiencing discrimination (57%), compared to the overall average of 69%, suggesting more pervasive experiences of sex-based discrimination within this group. The pattern indicates that ethnicity and migration status intersect with gender to create heightened vulnerability to discriminatory treatment.

5.9.2 Ethnicity-based Discrimination

===  ETHNICITY-BASED  DISCRIMINATION ANALYSIS ===
Total valid responses for Inhouse_Discrimination_Ethnicity : 1717 

The ethnicity-based discrimination analysis reveals the highest discrimination rates among all forms examined, with Black workers and workers born outside the UK both experiencing discrimination at 52.5% and 52.3% respectively. Asian workers also face substantial discrimination (45.6%), while the overall rate among all outsourced workers is 30.0%. The data shows particularly concerning patterns for Black workers, with only 48% reporting “Never” experiencing ethnicity-based discrimination, compared to 70% overall. Similarly, workers born outside the UK show only 48% reporting “Never” experiencing discrimination. Notably, Black workers report the highest rates of frequent discrimination, with 24% experiencing it “Sometimes” and 10% “Often”. This pattern suggests that ethnicity-based discrimination is the most pervasive form of discrimination faced by outsourced workers, with Black workers and migrants bearing the heaviest burden.

5.9.3 Age-based Discrimination

===  AGE-BASED  DISCRIMINATION ANALYSIS ===
Total valid responses for Inhouse_Discrimination_Age : 1730 

The age-based discrimination analysis shows moderate discrimination rates across demographic groups, with Black workers experiencing the highest rates (44.9%), followed by workers born outside the UK (44.1%). Asian workers report discrimination at 35.8%, while the overall rate among all outsourced workers is 35.2%. Female workers experience age-based discrimination at 34.6%. The data reveals that Black workers have the lowest percentage reporting “Never” experiencing discrimination (55%), compared to 65% overall. Workers born outside the UK also show elevated vulnerability with 56% reporting “Never” experiencing discrimination. Age-based discrimination appears to be the second most common form of discrimination after ethnicity-based discrimination, with Black workers and migrants again showing heightened exposure to discriminatory treatment.

5.9.4 Disability-based Discrimination

===  DISABILITY-BASED  DISCRIMINATION ANALYSIS ===
Total valid responses for Inhouse_Discrimination_Disability : 1713 

The disability-based discrimination analysis shows lower overall discrimination rates compared to other forms, with workers born outside the UK experiencing the highest rates (31.9%), followed by Asian workers (27.5%) and Black workers (25.1%). The overall rate among all outsourced workers is 22.5%, while female workers report the lowest rate at 20.0%. The data shows that workers born outside the UK have the lowest percentage reporting “Never” experiencing discrimination (68%), compared to 77% overall. Notably, Asian workers show relatively high rates of frequent discrimination, with 14% experiencing it “Sometimes” and 6% “Often”. While disability-based discrimination is the least prevalent form among those examined, it still affects nearly one in four outsourced workers overall, with migrant workers showing particular vulnerability.

5.9.5 Nationality-based Discrimination

===  NATIONALITY-BASED  DISCRIMINATION ANALYSIS ===
Total valid responses for Inhouse_Discrimination_Nationality : 1726 

The nationality-based discrimination analysis reveals high discrimination rates that mirror the ethnicity-based patterns, with Black workers and workers born outside the UK both experiencing discrimination at 52.5% and 52.4% respectively. Asian workers face substantial discrimination at 43.1%, while the overall rate among all outsourced workers is 31.0%. Female workers report discrimination at 28.7%. The data shows that Black workers and workers born outside the UK both have only 48% reporting “Never” experiencing discrimination, compared to 69% overall. Black workers and workers born outside the UK also show the highest rates of frequent discrimination, with Black workers experiencing it “Sometimes” (24%) and “Often” (7%), while workers born outside the UK report similar patterns (21% “Sometimes”, 8% “Often”). The pattern confirms that nationality-based discrimination closely parallels ethnicity-based discrimination, suggesting these forms of discrimination are interconnected and particularly target migrant and ethnic minority workers.

5.10 Clarity Questions Analysis

We also explored workplace clarity among outsourced workers across eleven key dimensions including clarity about reporting structures for pay problems, rights and entitlements, time-off approval processes, promotion pathways, and role responsibilities. The analysis also assesses communication effectiveness between organisations, workers’ confidence in raising workplace improvements, and management responsiveness to discrimination, bullying, and racism complaints. Descriptive statistics and cross-tabulations with demographic variables identify patterns in workplace clarity and potential disparities in organisational transparency across different groups of outsourced workers.

Clarity Questions Summary Table:

Question Area	Total Responses	Strongly Agree (%)	Somewhat Agree (%)	Neither (%)	Somewhat Disagree (%)	Strongly Disagree (%)
Role responsibilities	1,814	48.2	31.1	13.6	4.7	2.3
Management handles racism	1,814	44.4	28.8	17.3	5.7	3.8
Pay problems contact	1,814	40.6	33.1	16.8	6.4	3.1
Time off approval	1,814	40.6	34.1	16.3	6.3	2.7
Management handles bullying	1,814	40.5	28.1	19.5	7.5	4.4
Rights/entitlements contact	1,814	37.4	34.1	17.4	7.6	3.5
Management prevents discrimination	1,814	37.0	30.5	20.8	8.1	3.6
Organisational communication	1,814	36.1	33.5	19.7	7.1	3.6
Promotion contact	1,814	35.3	32.0	19.7	7.6	5.3
Can suggest improvements	1,814	34.2	32.9	21.5	8.1	3.2
Opinion will be respected	1,814	31.4	34.5	22.3	7.3	4.5

=== CLARITY QUESTIONS BY INCOME GROUP ===

Clarity by Income Group Summary:

Income Group	Question Area	Total	Agree (%)	Disagree (%)	Neither (%)
High	Can suggest improvements	336	67.9	11.6	18.1
Mid	Can suggest improvements	831	65.8	11.0	22.1
Low	Can suggest improvements	393	60.3	12.7	24.1
High	Management handles bullying	336	71.4	6.2	17.7
Mid	Management handles bullying	831	65.2	13.1	18.9
Low	Management handles bullying	393	54.5	13.7	25.3
High	Management handles racism	336	74.7	6.5	13.1
Mid	Management handles racism	831	69.3	11.0	16.0
Low	Management handles racism	393	59.0	8.7	24.2
High	Management prevents discrimination	336	73.5	6.2	17.8
Mid	Management prevents discrimination	831	63.7	13.2	20.0
Low	Management prevents discrimination	393	55.0	13.2	26.2
High	Opinion will be respected	336	72.6	8.0	17.6
Mid	Opinion will be respected	831	63.3	12.6	22.8
Low	Opinion will be respected	393	55.5	13.7	27.1
High	Organisational communication	336	74.4	9.8	13.5
Mid	Organisational communication	831	66.4	10.6	21.6
Low	Organisational communication	393	59.3	12.5	25.4
High	Pay problems contact	336	74.7	6.8	15.7
Mid	Pay problems contact	831	70.8	10.7	17.0
Low	Pay problems contact	393	67.7	11.2	18.0
High	Promotion contact	336	72.0	8.9	17.3
Mid	Promotion contact	831	63.8	14.6	19.2
Low	Promotion contact	393	56.5	15.3	23.0
High	Rights/entitlements contact	336	72.9	10.1	14.9
Mid	Rights/entitlements contact	831	69.9	11.7	17.1
Low	Rights/entitlements contact	393	61.6	12.0	23.3
High	Role responsibilities	336	80.1	6.8	10.7
Mid	Role responsibilities	831	77.3	7.2	14.5
Low	Role responsibilities	393	74.6	7.9	14.5
High	Time off approval	336	77.1	8.9	11.6
Mid	Time off approval	831	71.7	9.3	17.2
Low	Time off approval	393	70.7	8.7	18.1

Statistically Significant Results (p < 0.1):

Factor	Effect	95% CI Lower	95% CI Upper	Sig.	P-value
Age (per year)	0.007	0.004	0.010	***	<0.001
Not born in UK (vs Born in UK)	-0.239	-0.358	-0.120	***	<0.001
Black/African/Caribbean (vs White British)	0.310	0.116	0.504	**	0.002
High income (vs Mid income)	0.166	0.054	0.278	**	0.004
Low income (vs Mid income)	-0.148	-0.249	-0.047	**	0.004
Region: Wales	0.263	0.060	0.466	*	0.011
Region: Yorkshire and the Humber	0.225	0.042	0.408	*	0.016
White Other (vs White British)	0.214	0.022	0.406	*	0.029
Region: North East	0.284	0.024	0.543	*	0.032
Region: Northern Ireland	-0.279	-0.556	-0.003	*	0.048

The clarity analysis reveals significant variation in workplace transparency across different domains and income groups. Role responsibilities show the highest clarity (77.5% agreement), followed by time off approval (72.8%) and pay problems contact (71.6%), indicating that basic operational processes are generally well-understood. However, areas requiring higher-level organisational engagement show concerning gaps: opinion will be respected (64.1%), management prevents discrimination (64.4%), and promotion contact (64.7%) have the lowest agreement rates.

Income-based disparities are particularly stark, with high-income workers consistently reporting greater clarity than low-income workers across all domains. The largest gaps appear in management prevents discrimination (18.5 percentage point difference), opinion will be respected (17.1 points), and management handles bullying (16.9 points). These patterns suggest that organisational transparency and confidence in management responsiveness decrease substantially as income levels fall, potentially reflecting differential treatment or reduced organisational investment in communication with lower-paid outsourced workers. The consistency of these income-based disparities across multiple domains points to systemic inequalities in workplace clarity rather than isolated communication issues.

5.11 Work Preference by Income Group

Finally we examine the relationship between income levels and work preferences among outsourced workers, comparing preferences for in-house versus outsourced employment across low, mid, and high income groups. The analysis employs cross-tabulations and statistical tests to identify whether income level influences workers’ preferences for employment arrangements, with particular attention to understanding how economic circumstances may shape attitudes toward outsourcing.

Work Preference by Income Group Cross-tabulation:

label	variable	income_group			Total
		Mid Income	High Income	Low Income
Work_Preference	I have no preference	342 (41.2%)	140 (41.7%)	162 (41.2%)	644 (41.3%)
	I would prefer to be an in-house worker	145 (17.4%)	57 (17.0%)	56 (14.2%)	258 (16.5%)
	I would prefer to be an outsourced worker	90 (10.8%)	33 (9.8%)	38 (9.7%)	161 (10.3%)
	I would strongly prefer to be an in-house worker	130 (15.6%)	61 (18.2%)	52 (13.2%)	243 (15.6%)
	I would strongly prefer to be an outsourced worker	54 (6.5%)	27 (8.0%)	20 (5.1%)	101 (6.5%)
	Not sure	70 (8.4%)	18 (5.4%)	65 (16.5%)	153 (9.8%)
	Total	831 (53.3%)	336 (21.5%)	393 (25.2%)	1560 (100.0%)

=== Statistical Analysis: Work Preference by Income Group ===

Statistical Test Results: Work Preference by Income Group
Statistic	Value
Test Method	Fisher's Exact Test (simulated)
Sample Size	1560
P-value	< 0.001
Significance	***
Cramér's V	0.104
Effect Size	Small
Method Details	Monte Carlo simulation with 10,000 replicates

=== Summary Statistics ===

Key findings:

- Low income: 27.4% prefer in-house, 14.8% prefer outsourced
- Mid income: 33.0% prefer in-house, 17.3% prefer outsourced
- High income: 35.2% prefer in-house, 17.8% prefer outsourced

The work preference analysis reveals clear patterns in employment arrangement preferences across income groups, with statistical significance confirmed by Fisher’s exact test (p < 0.001, Cramér’s V = 0.104, small effect size). No preference dominates across all income groups (approximately 41%), but meaningful differences emerge in definitive preferences and uncertainty levels.

Income-based preference patterns show that higher-income workers increasingly prefer in-house employment: low-income workers prefer in-house work at 27.4%, rising to 33.0% for mid-income and 35.2% for high-income workers. Conversely, outsourced work preferences remain relatively stable across income groups (14.8% to 17.8%), suggesting that income level primarily influences attraction to in-house employment rather than satisfaction with outsourcing.

The most striking pattern appears in uncertainty levels, where low-income workers show dramatically higher “not sure” responses (16.5%) compared to mid-income (8.4%) and high-income workers (5.4%). This 11.1 percentage point gap between low and high-income groups suggests that economic insecurity may contribute to greater uncertainty about employment preferences, potentially reflecting limited exposure to alternative employment arrangements or anxiety about job security that makes definitive preferences more difficult to form. The consistent 2:1 ratio favoring in-house work across all income groups indicates that while income influences preference strength, the fundamental appeal of in-house employment remains broadly consistent.

6 Limitations and Future Research?

• Discuss the limitations of the data and the analysis
• If something is really burning we can suggest some further analysis that people can do

7 Reproducibility

All analyses presented in this report can be fully reproduced using the code and data provided in the Just Knowlegde GitHub repository.

---

8 Appendices

8.1 Study 1 - Age

The table below shows weighted descriptive statistics of the sample, and the figure below shows the frequency of respondents at each single year of age.

Mean	Median	Min	Max	Standard dev.
42.1	42	16	80	13.17

8.2 Study 1 - Gender

The table below shows the weighted gender breakdown of the sample

Gender	Weighted frequency	Weighted percentage
Male	4957.18	48.82
Female	5117.61	50.39
Other	15.37	0.15
Prefer not to say	64.84	0.64

8.3 Study 1 - Ethnicity

The table below shows the weighted ethnicity breakdown using the full range of Census 2021 categories. Note that ‘NA’ indicates non-responses.

Ethnicity	Weighted frequency	Weighted percentage
English / Welsh / Scottish / Northern Irish / British	7732.24	76.14
Irish	113.61	1.12
Gypsy or Irish Traveller	10.79	0.11
Roma	7.49	0.07
Any other White background	479.38	4.72
White and Black Caribbean	58.75	0.58
White and Black African	35.04	0.35
White and Asian	41.52	0.41
Any other Mixed / Multiple ethnic background	49.49	0.49
Indian	311.73	3.07
Pakistani	149.94	1.48
Bangladeshi	76.50	0.75
Chinese	145.53	1.43
Any other Asian background	163.15	1.61
African	227.05	2.24
Caribbean	71.67	0.71
Any other Black, Black British, or Caribbean background	37.39	0.37
Arab	32.50	0.32
Any other ethnic group	30.46	0.30
Don’t think of myself as any of these	8.81	0.09
Prefer not to say	30.45	0.30
NA	341.51	3.36

We also make use of an aggregated ethnicity variable that groups ethnicities into fewer categories. The table below shows how the Census categories map onto the aggregated categories.

Census categories	Aggregated categories
English / Welsh / Scottish / Northern Irish / British	White British
Irish	White other
Gypsy or Irish Traveller	White other
Roma	White other
Any other White background	White other
White and Black Caribbean	Mixed/Multiple ethnic group
White and Black African	Mixed/Multiple ethnic group
White and Asian	Mixed/Multiple ethnic group
Any other Mixed / Multiple ethnic background	Mixed/Multiple ethnic group
Indian	Asian/Asian British
Pakistani	Asian/Asian British
Bangladeshi	Asian/Asian British
Chinese	Asian/Asian British
Any other Asian background	Asian/Asian British
African	Black/African/Caribbean/Black British
Caribbean	Black/African/Caribbean/Black British
Any other Black, Black British, or Caribbean background	Black/African/Caribbean/Black British
Arab	Arab/British Arab
Any other ethnic group	Other ethnic group
Don’t think of myself as any of these	Don't think of myself as any of these
Prefer not to say	Prefer not to say
NA	NA

The table below shows the weighted ethnicity breakdown using the aggregated set of categories

Ethnicity	Weighted frequency	Weighted percentage
White British	7732.24	76.14
Arab/British Arab	32.50	0.32
Asian/Asian British	846.86	8.34
Black/African/Caribbean/Black British	336.10	3.31
Don't think of myself as any of these	8.81	0.09
Mixed/Multiple ethnic group	184.80	1.82
Other ethnic group	30.46	0.30
Prefer not to say	30.45	0.30
White other	611.27	6.02
NA	341.51	3.36

8.4 Study 1 - Income by outsourcing and income group